Systems and methods of using wireless location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior

ABSTRACT

Exemplary embodiments are disclosed of systems and methods of using location, context, and/or one or more communication networks for monitoring for, preempting, and/or mitigating pre-identified behavior. For example, exemplary embodiments disclosed herein may include involuntarily, automatically, and/or wirelessly monitoring/mitigating undesirable behavior (e.g., addiction related undesirable behavior, etc.) of a person (e.g., an addict, a parolee, a user of a system, etc.). In an exemplary embodiment, a system generally includes a plurality of devices and/or sensors configured to determine, through one or more communications networks, a location of a person and/or a context of the person at the location; predict and evaluate a risk of a pre-identified behavior by the person in relation to the location and/or the context; and facilitate one or more actions and/or activities to mitigate the risk of the pre-identified behavior, if any, and/or react to the pre-identified behavior, if any, by the person.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit and priority of U.S. ProvisionalPatent Application No. 62/435,042 filed Dec. 15, 2016 and U.S.Provisional Patent Application No. 62/480,206 filed Mar. 31, 2017. Theentire disclosures of the above applications are incorporated herein byreference.

FIELD

The present disclosure generally relates to systems and methods of usingwireless location, context, and/or one or more communication networksfor monitoring for, preempting, and/or mitigating pre-identifiedbehavior.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

Addiction to substances, such as alcohol and drugs, and activities, suchas gambling, are a major scourge of society. Addictions can come in manyforms, but generally can be put into two categories: 1) addiction to asubstance, such as drugs, alcohol, or food, or 2) addiction to anactivity, such as gambling, sex, or shopping. The human impact of anaddiction can vary greatly in terms of physical toll on the mind andbody as well as everyday life-damage such as destruction of families andjob loss. Common life-ruining addictions include those involvingalcohol, prescription and non-prescription drugs, cigarettes/nicotine,and gambling. Less common but very serious addictions involveoverindulging in sex, eating, and avoidance/lack of food (e.g., anorexiaor bulimia). Other addictions typically (but not always) can beconsidered relatively minor or annoying such as shopping, exercise,work, sports viewing, beauty enhancement/plastic surgery, videogames, oreven surfing the Internet or constant use of smartphones, to name apartial list.

The term addiction has many definitions, but in general it refers to aperson or persons who cannot, or will not, stop using or doing somethingthat is potentially harmful to them and/or others around them. Whileaddiction often conjures images of drunks or drug addicts roaming thestreets, in reality addiction impacts all walks of life, fromprofessionals to blue-collar workers to athletes to celebrities tostay-at-home parents, even to children. Many addicts live otherwiseuseful, functional lives, that can be greatly improved if theiraddiction is effectively treated.

Various types of addiction treatments, programs, and other methods foraddressing addiction have been around for many decades. Examplesinclude: 12-Step Programs such as Alcoholics Anonymous (AA),Acupuncture, Aversion Therapy (multiple forms), Behavioral Self-ControlTraining, Cognitive Therapy, Going Cold Turkey, Community Reinforcement,Diet-based Programs, Drug-based Treatments (multiple forms),Exercise-based programs, Hypnosis, Interventions, Meditation,Motivational programs, Nutrition-based programs, Rehabilitation(Inpatient and Outpatient)/Hospitalization stays, Religious-basedprograms, Self-Change Manuals/Guides, (Traditional) Psychotherapy(multiple forms), Spiritual Immersion, and Work/Treatment programs toname some of the more commonly-known approaches.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

Exemplary embodiments are disclosed of systems and methods of usinglocation, context, and/or one or more communication networks formonitoring for, preempting, and/or mitigating pre-identified behavior.For example, exemplary embodiments disclosed herein may includeinvoluntarily, automatically, and/or wirelessly monitoring/mitigatingundesirable behavior (e.g., addiction related undesirable behavior,etc.) of a person (e.g., an addict, a parolee, a user of a system,etc.). In an exemplary embodiment, a system generally includes aplurality of devices and/or sensors configured to determine, through oneor more communications networks, a location of a person and/or a contextof the person at the location; predict and evaluate a risk of apre-identified behavior by the person in relation to the location and/orthe context; and facilitate one or more actions and/or activities tomitigate the risk of the pre-identified behavior, if any, and/or reactto the pre-identified behavior, if any, by the person.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 is a diagram of an example system for determining location andcontext of an addict, a support network, and other information andaspects of an addict's personal and professional life for addictiontreatment purposes, including relapse prevention and containment. Thisdiagram includes various example networks and technologies that may beused for collecting and analyzing the addict's location and context.Also shown are example data sources and analytical engines that may beneeded to process such data and to identify and implement actions topreempt, prevent, and/or contain any relapse.

FIG. 2 describes an example Addict Monitor/Controller (AMC) device thatmay be used to collect, process, and disseminate context and addictiontrigger-related data from and about an addict via various sensors andother data collection mechanisms, and to interface with/to the addictand 3rd party mechanisms. The device may also provide mechanisms toprovide feedback to the addict and assist in the implementation ofrelapse-related preventative and containment actions.

FIG. 2a provides examples of distributed sensor deployment, datacollection options, localized sensors, and localized networks that maybe used in exemplary embodiments.

FIG. 2b provides examples of internet of things (IoT) addict-relatedsensors, devices, and networks that may be used in exemplaryembodiments.

FIG. 3 depicts example steps for monitoring an addict's triggers, and inthe course of doing so assessing/predicting the addict's risk ofrelapse. FIG. 3 also describes identifying possible resources that couldhelp the addict, and the actions that could be taken to prevent, preemptor contain a relapse. FIG. 3 also describes an example process forselecting such resources and actions.

FIG. 4 depicts an example system and example process for determining thelocation/context of an addict as well as the location/context of supportresources using a variety of sensors and other information sources.

FIG. 5 describes an example system and example process for assessing anaddict's trigger/relapse risk. FIG. 5 also describes how such algorithmscould be made self-learning to better assess an addict's relapse risk.

FIG. 5A depicts an example embodiment of a method for managing damagecontrol and recovering from a relapse situation.

FIG. 5B provides examples of risk, support areas maps, and map mashups.

FIG. 6 depicts example ways to identify/monitor trigger/relapse risk andidentifying, selecting, and implementing support resources and actions.

FIG. 6A depicts an exemplary embodiment of a trigger monitoring feedbackand learning system.

FIG. 7 describes example ways to identify/determine and select the bestactions and resources when relapse risk is high.

FIG. 7A describes an example of an action-determining subprocess—specifically, ways to utilize regularly scheduled addictcommunity meetings or spontaneous, unscheduled, flash addict communitymeetings.

FIG. 8 describes example ways to select the best interface(s) forinteracting with an addict, including implementing relapse preventionactions.

FIG. 9 describes an example addict rewards/demerits system based on anaddict's behaviors and actions, which may include rewarding (orpunishing) an addict based on behavior via tracking and data analyticsand various reward mechanisms.

FIG. 10 describes example ways in which addicts can receive and transmitsobriety ideas in public and private places via beacons. FIG. 10 alsoillustrates example ways in which Real-Time Location System (RTLS)technologies can be used to enable ad hoc, spontaneous, unscheduled, orflash addict meetings between people with similar addiction issues.

FIG. 11 thru 14 describe examples of using location and/or contextinformation to provide privacy and security for data collected invarious implementations of the present disclosure.

FIG. 15 depicts an example embodiment of a method for monitoring for arisk of a pre-identified behavior (e.g., pre-identified addict-relatedundesirable behavior, etc.). FIG. 15 also includes example triggers,priorities, and initial risk assessment/detection sensors.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

Sometimes addiction treatments such as those mentioned above work. Butvery often addiction treatments do not work, at least not for long.While there are many reasons why an addiction treatment might not work,one common reason is that most treatments are most effective when theyare actively being implemented, such as when a person is actively inresidence in a treatment facility, attending an AA meeting, or in atherapy session. Put another way, one of the vulnerabilities of theseand many of the above approaches is that their effectiveness is closelytied to their immediacy, both in terms of physicality to the addict (fortreatments that have a personal counseling and/or physical locationelement) and in time—how fresh the teachings of the program are in themind of the addict, not to mention how long the addict is willing toactively participate in treatment or treatment-related activities. Thisis particularly true for many of the most common treatments, such asrehab facility stays (inpatient or outpatient), therapy sessions, oralcoholism-related community meetings. Once the addict leaves those(usually physical, but increasingly virtual or augmented) places wherethe treatment takes place, the lessons or motivations from thosetreatments become weaker or start to fade, while at the same timeopportunities and temptations to partake in the addiction increase.

Another issue with the above treatments is that many surprisingly placelittle emphasis on the triggers that may cause or set off one'saddiction particularly in understanding what situations, circumstances,or mindsets (broadly, triggers) make or drive the addict to want to usethe substance and/or activity in question. Triggers are what very oftendrives the desire to use. By way of background, triggers may besituations, circumstances, activities, events, and mental thoughtprocesses and frames-of-mind that tempt or cause an addict to want touse a substance or engage in an activity. Without such detailedknowledge of the triggers of addiction, such treatments often focus onsymptoms, or controlling such triggers after they occur. In any event,even in treatment programs where understanding of addiction triggers areemphasized, such emphasis is usually focused on general understanding ofthe triggers, not in dealing with them on a practical, day-to-day,hour-by-hour basis of everyday life.

As the addict gets farther away from the treatment program physicallyand/or in time the lessons from those programs naturally start to fade,becoming less effective, and, in turn, making the addict moresusceptible to relapse. Thus, it is desired to find ways of keepingtreatment lessons fresh in the mind of the addict, or alternativelyrefreshing them in a way to actively deter an addict from relapsing. Itis also unfortunately true that even the best-intentioned addict cannotsee all the possible situations that might tempt or trigger the addictto relapse in time to avoid such triggers, or at least mitigate them theaddict may find himself in a high-risk environment before realizing it.After recognizing the above, the inventor hereof has further recognizeda need for a way to use technology to provide treatment reinforcementwhen the addict is away from the place of primary treatment(s), whilesimultaneously protecting the addict from the temptations/addictiontriggers of their addiction.

As mentioned earlier, some, but not all of the treatment programs aboveinclude education about addiction triggers. Such triggers includesituations, circumstances, activities, events, and mental thoughtprocesses and frame-of-minds that tempt or cause an addict to want touse a substance or engage in an activity known or identified to bedetrimental to the addict and/or others. Examples of triggers includebut are not limited to: Anger, Anxiety, Boredom, Change, Children,Conflict, Depression, Disorder, Embarrassment, Escape, Envy, Excitement,Fun, Frustration, Guilt, Health issues, Holidays Hunger, Insomnia, Jobstress, Loneliness, Mid-life Crises, Money worries, Noise,Overconfidence, Pain, Peer Pressure, feeling Powerful or Powerless,Proximity (to the substance), Fear of Quitting (the substance oractivity), Relationship issues, Relatives, Reminders, Sex, ShoppingSituations, Social Situations, Special Occasions, Stress, Taste andSmell, Times of Day, being Tired, being Unfun, being a Victim (or crime,abuse), ex-spouses/partners, Yelling, even Season or Weather changes orMusic. There are potentially hundreds of possible addiction triggers,and many thousands of trigger combinations.

It is when an addict's most vulnerable triggers become active or arepresent that the addict is most vulnerable to relapse. Thus, if boredomis a major trigger for an addict, it is imperative to keep the addictfrom becoming bored, or failing that, to respond to, and correct a boredaddict before the temptation to use a substance or engage in anaddictive activity becomes too strong and relapse occurs. Determining anaddict's triggers and proscribing actions and activities to deal withthose triggers without relapsing is anticipated to become a more commonand important part of many addiction treatments, and is at the core ofvarious exemplary embodiments of the present disclosure. In particular,various exemplary embodiments disclosed herein focus significantattention on understanding an addict's triggers and, in particular,using an array of sensors and other information to anticipate and/ordetect triggers that are active, present, or in danger of becomingactive or present, particularly by using location and context to a)anticipate, predict, and/or pre-empt an addict's triggers from becomingactive or present, b) prevent a relapse when triggers do become activeby initiating one or more actions, activities, and/or contacts with anaddict's support network, and/or c) contain or manage a relapse if andwhen its occurs.

Surprisingly, there is relatively little application of technology inaddiction treatment in the prior art, less that incorporates location,and practically nothing that utilizes context. For example, locationtechnology has been used to aid patient recovery, but this was focusedon physical rehabilitation of ambulatory (hospital) patients, notaddiction recovery nor patient monitoring outside a hospitalenvironment. Location technology has also been used as a small part ofan Internet addiction treatment, which focuses primarily onunderstanding the amount and type of internet activity that is takingplace (e.g., games, certain types of websites, etc.). This latterexample also utilized the concept of support groups, but not in a mannerthat emphases location or context of the addict or the support group.

Exemplary embodiments of addiction treatment systems and methods aredisclosed herein. One example embodiment of a system includes aplurality of user devices, sensors, and other technology to: determine,through one or more communications networks, the location of an addictand the context of the addict at the location; evaluate a risk ofrelapse by the addict in relation to the location and/or the context;facilitate one or more actions and/or activities to mitigate the risk,if any, and/or react to a relapse, if any, by the addict. By way ofexample, the context may include a situation, environment, and/or stateof mind of the addict at the location. The context may include why theaddict is at the location, who the addict is with at the location, whatthe addict is doing at the location, when (day/time) the addict is atthe location, and/or how the addict got to the location, etc.

One example embodiment of a method of treating an addict for anaddiction includes determining, through one or more communicationsnetworks, the location of an addict and the context of the addict at thelocation, where the context includes a situation, environment, and/orstate of mind of the addict; evaluating a risk of relapse by the addictin relation to the location and/or the context; and facilitating one ormore actions and/or activities to mitigate the risk, if any, and/orreact to a relapse, if any, by the addict. This may be done primarily bydetermining the location and context of the addict and assessing thatdata relative to the addict's addiction triggers.

In various exemplary embodiments, a plurality of user devices, sensors,and/or other technologies are provided to protect privacy and securityof information collected as disclosed herein. In some exemplaryembodiments, experience-based data, including but not limited tolocation and/or context data, may be used to condition access toprotected information. Access to the protected information may bepermitted to a permittee based on recognition by the permittee of theexperience-based data.

The distinction between location and context and its importance shouldbe noted. The location of a person is often (not always) part ofdetermining a person's broader context. With respect to exemplaryembodiments of the present disclosure, depending on the circumstances ofan individual, the location of the person may be most important; inother circumstances, the broader context may be more important. Forexample, if the methods and/or apparatus described in the presentdisclosure determine that the best course of action may be to attend anearby AA (Alcoholics Anonymous) meeting, the specific circumstances ofthat person at that time may dictate that the addict should attend thenearby AA meeting. In another exemplary embodiment, themethods/apparatus of the present disclosure may determine that for theaddict's current situation and addiction inducing trigger level (e.g.,anxiety trigger, etc.), the addict should meet a certain person in theaddict's support network who also has the same or similar issues (e.g.,anxiety and alcoholism etc.) issues at an (e.g., anxiety-related, etc.)meeting that is quite a bit farther away than the addict's currentlocation. Here, the context of the addict is more important than justthe addict's location.

In various embodiments of the present disclosure, addicts can be helpedto detect and deal with the triggers that initiate or enhance thecraving to indulge in their addiction. There generally are many suchtriggers, including but not limited to: Anger, Anxiety, Boredom, Change,Conflict, Depression, Disorder, Envy (desire to) Escape, Excitement,Extreme Emotions, Fear, Frustration, Guilt, Health problems, Holidays,Hunger, Insomnia, Job issues, Kids/Children, Loneliness, Media (TV,Radio, the Internet) marketing, Mid-Life Crisis, Money problems, Music,Noise, Overconfidence, Peer Pressure, Power, Powerlessness, Proximity(to an addictive substance), (fear of) Quitting, Relationships,Relatives, Reminders, Sex, (change of) Seasons, Smell, SocialSituations, Stress, Taste, Times of Day, (being) Tired, (feeling) NotFun or Unhappy, (feeling) Victimized, Weather, Yelling, and Zeal (highenergy). Many addicts are especially vulnerable to relapsing when facedwith one or more of these triggers. Some of these triggers have alocation dimension to them, most notably proximity to an addictionsubstance or activity, and many more have a location element in theactions or solutions for dealing with those triggers (and/or high-riskfor relapse situations) without relapse. For example, a response to thedetection of the Boredom trigger may require the addict to go to acertain place to do a certain activity. Loneliness would involvevisiting with or visit by a member of the addict's support network.Noise could require going to a quiet place to meditate, such as a churchor library, or to retreat to a serene program on a virtual realitydevice. This kind of information could be captured in a data profile(e.g., an addict profile including actions to take in relation to theaddict, etc.) stored in a database or similar data store.

The present disclosure includes various exemplary embodiments of systemsand methods that utilize the location and context of an addict and otherresources to a) preempt trigger and/or high risk relapse situations, b)prevent relapse in high risk situations, and/or c) respond to, manage,and recovery from a relapse when they do occur. Various embodimentsinclude collecting, aggregating, and analyzing addict- andaddiction-related data specific to that addict's condition,vulnerabilities, motivations, and usage triggers. Such data/informationcan be collected from a wide variety of sensors and other data sources,including but not limited to: personal devices such as smartphones,tablets, computers, PDAs, wearables (data collection devices worn on theperson, such as Fitbit, etc.), implants, Google Glass, etc.; nearbysensors or devices such as security/video cameras, smart devices (suchas smart home-related sensors, etc.), crowdsourcing data collectionapplications of nearby users, building/store/office Wi-Fi networks,location-sensitive beacons, etc.; and/or extended data collectionmechanisms such as road traffic sensors, public video cameras orbillboard displays, weather data collection sensors, lawenforcement/security-related devices, etc.

Various system and method embodiments according to the presentdisclosure make use of trigger-based sensor networks and trigger-basedsupport networks that may be tuned or modified so as to collect datapotentially related to one or more particular addiction triggers, suchas Anger, Frustration, Noise, Social Situations, Stress, Yelling, etc.Such data solely or in combination can identify various high risk (ofaddiction usage) contexts or relapse situations, circumstances, events,and/or possible mental frame-of-mind/thought processes that often haveto be managed to allow the addict the ability to successfully deal withsuch situations without succumbing to the addiction(s). This managing ofsuch situations may include providing, recommending, and/or injectingactions, activities, resources, recommendations, directions, and/orelements of control into the addict's life on either an ad-hoc,occasional, periodic, and/or (near) continuous manner to help the addictto refrain from their addiction. Management of such situations can bedone via a variety of analysis, assessment, and prediction engines andalgorithms that anticipate or predict the impact of certain situations,contexts, circumstances, or events on an addict's behavior and overallsobriety and devise and quickly put in place a course of action tominimize the addict's risk of relapse, or failing that, minimizing anyresultant harm and damage. Such a course of action may be predominatelylocation-based, meaning using location information as a key part of thecourse of action, but the present disclosure is not limited tolocation-based information; key information may well includenon-location based elements, particularly the use of sensors that canprovide valuable input into understanding the current context of theaddict, and actions that may have little or nothing to do with anaddict's location (such as the addict calling a family member to discusshis Frustration, for example).

While various aspects of exemplary embodiments of the present disclosureare targeted at the treatment of addiction, actual addiction is notalways involved. As noted before, exemplary embodiments of the presentdisclosure can be used for the prevention of addiction, dealing withpossible or actual use or misuse of substances and/or activities thatcan potential be harmful to individuals or groups, or indeed unrelatedto any addiction or substance/activity use/misuse at all. In order toutilize and receive the benefits of exemplary embodiments of the presentdisclosure, a person does not necessarily have to be an addict with anaddiction to a substance and/or activity. For example, a person may wantto cut back on drinking, for example, even if not physically addictedthereto or even if the person does not drink often. Treatment may referto any assistance provided in various exemplary embodiments to help anaddict and/or others in dealing with the addiction (as described broadlyherein) in some form or fashion, and/or for informational purposes.Sober may refer to non-usage of the addiction substance/activity.Relapse may refer to usage of the addiction substance/activity.

Exemplary embodiments of the present disclosure may also be applicableto persons and/or situations in a pre-addiction situation or scenariowhere the substances/activities are merely abused, that is, done more ormore often than might be considered acceptable, healthy, or desirable.Thus, various embodiments of the present disclosure may be applicable inrelation to persons who may be considered high-risk, such as thechildren of alcoholics, though no symptoms of addiction exist. Variousembodiments of the present disclosure further may be applicable inrelation to situations and/or scenarios where person(s) are neitheraddicted nor considered abuser(s) nor high-risk; rather, they and/orothers would like to reduce the usage of a substance or activity toachieve some real or perceived benefit. The above situations/scenariosand other applicable contexts may be generally included under the termaddiction, and a person suffering from addiction, abuse, or the generaldesire to reduce/stop doing some substance and/or activity may also bereferred to as an addict. Also, an addiction or addict is not limited tojust one substance or activity; many addicts concurrently or seriallysuffer from more than one addiction (sometimes referred to as dualdiagnosis, though it can actually be 2 or more addictions). Variousembodiments of the present disclosure can be equally applicable to suchpersons with multiple addictions, either concurrent or consecutive.

In addition, a distinct set of exemplary embodiments related to prisonerand particularly parolee tracking and monitoring is enabled by thepresent disclosure. Such tracking and monitoring is currently done by aGPS bracelet attached to the person's ankle or other body part, withtheir movements then monitored via GPS readings. In situations where GPSdoes not work or work well, particularly in a building or otherstructure or environment where GPS does not work, cell towertriangulation is employed to provide a rough calculation. Both havelimitations: GPS with it primary use cases of being tracked outdoors,and the inaccuracies associated with cell tower triangulation for indoorsituations.

Because of these limitations, GPS bracelets in effect determine the typeof prisoner/parolee tracking that can be done, limiting the person to aparticular area or building. It cannot get more “micro” than that and/orcannot granularly track the person's location and activities in anindoor environment, which is, however, possible with exemplaryembodiments of the present disclosure. For example, a parolee could beconfined to house arrest in a multi-unit apartment or motel due to theenhanced ability to track a person's movements indoors. In addition,activities can be monitored or controlled, such as a person on parolefor a DUI (Driving Under The Influence) being prohibited from drinking.The sensors associated with the present disclosure as well as riskcalculation algorithms could be used to detect high risk situationswhere the parolee is about to violate parole, generating various alarms.If the parolee followed through and drank, then exemplary embodiments ofthe present disclosure would provide the evidence needed to revokeparole.

Context may generally refer to an addict's situation, environment,and/or state-of-mind (e.g., as determined by biometric data, etc.)particularly as it relates to a potential substance abuse relapse.Traditionally in mobile systems, a person's physical location can form akey cornerstone of that person's context and indeed may be all that isneeded to determine the person's overall context in many instances. Forexample, if an alcoholic has stopped at a bar on his way home from work,it typically takes no additional data to infer a high-risk relapsesituation and state-of-mind. However, other or additional sensors may beused to confirm and/or refine a person's context. For instance, a lightsensor on an addict's device may indicate that the person is stilloutdoors (perhaps debating himself in the parking lot). A breathalyzersensor could indicate a relapse that the situation has moved fromneeding a prevention set of actions to a set of damage control andsafety actions. Thus, an addict's location may be considered theentirety of the context (stopped at a bar), part of the context (not yetin the bar), or perhaps even not a key part of the context (e.g. theperson is drinking or is drunk, having left the bar, etc.). However, formany if not most embodiments, physical location plays at least a partialrole in determining a person's overall context; in addition, thelocation of support resources very often plays a key role in if and/orhow such resources may be employed.

In various exemplary embodiments of the present disclosure, systems andmethods are provided for pre-empting, anticipating, and/or detectinghigh risk addiction relapse situations and determining and implementingactions and activities to prevent a relapse from occurring, or in thecase of actual relapse minimizing the associated damage and returningthe addict to sobriety as soon as possible. Various mechanisms areprovided for determining and utilizing an addict's context particularlylocation—in assessing their risk of relapse, and utilize that context aswell as the relative locations and contexts of other resources todetermine and implement relapse preventative actions. In variousexemplary embodiments of the present disclosure, location/context-basedmechanisms are provided to minimize or contain the consequences in theevent that a relapse occurs, as well as mechanisms to preempt andprevent high risk situations from occurring

At a more granular level, an example communications network includes aplurality of heterogeneous, differing, or different types of sensingdevices configured to monitor the location and/or context of an addict;and a plurality of heterogeneous, differing, or different types ofinterface devices each configured to engage in interaction with theaddict, with a support person for the addict, and/or with a third partyin the event that the network detects a relationship between themonitored location and/or context and a trigger predetermined in thenetwork for the addict as being related to relapse; wherein theinteraction is selected based on the trigger and the monitored locationand/or context.

The example communications network may include one or more server,client, cloud, peer-to-peer, and/or other devices configured to developand/or update a profile of the addict based on monitoring data from thesensing devices and/or the interaction engaged in by one or more of theinterface devices.

In the above example communications network, one or more of the sensingdevices may be configured to detect and/or determine the relationshipbetween the trigger and the monitored location and/or context.

In the above example communications network, the sensing devices may belocated in, on, and/or near the addict, and/or elsewhere relevant to acurrent and/or future location/context of the addict.

In various aspects of the present disclosure, a network-implementedmethod of providing support for an addict includes monitoring thelocation and/or context of the addict, the monitoring performed by oneor more sensing devices; detecting a relationship between the monitoredlocation and/or context and a trigger predetermined in the network forthe addict as being related to relapse; and based on the detectedrelationship, one or more of a plurality of interface devices of thenetwork interacting with the addict, with a support person for theaddict, and/or with a third party.

The foregoing example method may include, based on the monitoring, thedetecting, the determining, and/or the interacting, developing and/orupdating a profile of the addict, the profile including actions to takein relation to the addict.

In the foregoing example method, one of the sensing devices may beconfigured to send the monitored location and/or context and/or thedetected relationship to one or more other devices of the network.

In the foregoing example method, the sensing devices may be one or moreof the following: located in, on, and/or in the vicinity of the addict,mobile, and stationary.

FIG. 1 is a high-level summary diagram that shows an addict withassociated devices, sensors, wearable/embedded tags, and other locatabletechnology. FIG. 1 also shows an exemplary scope of potential people,resources, assets, locations, applications, and data that may be helpfuland/or important in helping the addict become and stay sober. Associateddevices generally refers to any technology that may be directly orindirectly associated with the addict for collecting data on or aboutthe addict and/or for disseminating data or actions to or about theaddict. Thus, an addict does not have to be in physical contact with adevice for a given exemplary embodiment to work. For example, anassociated device could be the use of a drone to shadow an addict'smovements, location, and behavior and reporting that information back toanalytical engine(s) of the given embodiment. The scope of potentialresources is not limited to those listed in FIG. 1 as other resourcesmay be used in other exemplary embodiments.

FIG. 1 illustrates various example embodiments 100 of the presentdisclosure including the use of an addict's support network 102. Asupport network is generally considered people who have some knowledgeof and/or influence about the addict's problems and are prepared tohelp. The support network 102 of people may include medicalprofessionals, friends, family members, therapists, co-workers,spouses/partners, social workers, advocates, pastors, priests,rehab/treatment centers, emergency responders, lawyers, courts, paroleofficers, social/business networks, family support networks, specializedsocial media (Addiction, Trigger-Focused) support, other websites,internet/cloud help, addiction community members, other addicts (using,recovering) or anyone who might be aware of the addict's situation andin a position (including physical location) to assist the addict in someform if the need arises (e.g., a support person, etc.).

Various example embodiments of the present disclosure provide forcontinuous, periodic, ad-hoc, and/or as-needed monitoring 103 d of thesupport person's location and/or status for potentially helping one ormore addict's, such as but not limited to Busy, Work, Available, InEmergency, Please Find Someone For Me To Help (e.g. assist anyone, notjust the persons listed as approved assisters), and/or schedule/calendarfor one or more of those support persons and associated respectivestatuses. This monitoring could be initiated by the Addiction Server, bythe support person's device(s) 102 a, 102 b, 102 c, 102 d, 102 e,cloud-based services, and/or 3rd party applications that already makeuse of location and/or status monitoring.

While FIG. 1 shows a variety of networking and communicationstechnologies, systems, and architectures, such as wirelesscommunications, client-server, peer-to-peer, and cloud computing, thepresent disclosure is not limited to these. For example, an architecturemay be deployed that deploys disclosed functionality for a very limitedarea for a select type of receiver/person for a limited period of time,such as spontaneous, unscheduled, or flash (short-notice) drinkingtrigger meeting (a kind of specialized Alcoholics Anonymous meeting).Some or all of these attendees might have a specially issued RFID-type(printable, downloadable, or temporarily/specialty activated) tag,and/or beacon-based network that allows them access to the meeting andinterfaces with other personal technology that enables them to enjoybenefits from the meeting, such as personalized holographicpresentations to their personal visor, specialized drug doses, or justvalidation of their identity. Addiction FOB or dongle-type devices mightalso be used that serve as the interface means between such meetingtechnology and other personal technology such as a wearables orimplantable, or the Addiction Monitor/Controller device 200 shown inFIG. 2.

FIG. 1 also discloses an example Addiction Server/Cloud/Internet ofThings/Client Application(s) and Processing Networks 103 (server 103 a)that can be a key hub for communications with a variety of people 102,resources, assets, applications, and data sources that may haverelevance to the addict. As shown, the data sources may include adatabase 103 b of support network data (e.g., location,availability/schedule, specialties, privacy requirements or regulations,etc.) and a database 103 c of third party app data and interfaces (e.g.,social medial, local search, navigation, etc.) and affinity programs.The data sources may also include data sources accessible over a network118 (e.g., local network, public network, private network, internet,IOT, etc.) such as a database 127 of addict data (e.g., medical,professional, public records, media, etc.), a database 128 of localaddiction data (e.g., police reports, trends, etc.) and a database 129of local data feeds (e.g., events, traffic, news, weather, camera feeds,etc.). Additional data sources may include addict data sources includingaddict data and analytics 104, including predictive analytics data, etc.The addict data and analytics 104 may include privacy, security,rewards, motivational database(s) and engine(s) 105, action/responseengine, interface coordination database(s) and engine(s) 106,risk/relapse assessment/prediction, learning database(s) and engine(s)107, (trending) context and behavior inference database(s) and engine(s)108, addict profile, support network, schedule/calendar,devices/vehicles 109, addict usage triggers, hobbies, media posts,behavioral data 110, location/context profiles, historicallocation/context data 111, high risk locations, places of interests(POIs) suppliers, enablers 112, addict medical, personal data 113, andadministration, security, and verification 114.

The server 103 a also serves as the primary analytical engine fordeveloping and processing algorithms for profiling an addict's behavior,tendencies, risks, and probabilities of relapse for a wide range ofpossible situations, and for determining a variety of actions to, for,or on behalf of the addict to avoid relapse and/or improve the addict'soverall treatment. Included in potential actions are monitoring thelocation of the addict and the addict's support network for scenarioswhere one or more support persons may be dispatched to the addict'slocation, or vice versa.

Such server functionality can be physically and/or logically configuredin many forms. It can be centralized in one or more servers. It can bepartitioned in a centralized manner such that functionality is splitamong different servers, such as one server being a communicationsnetwork front-end for communicating with various addicts, devices,sensors, and other networks, while another server or set of servers doesthe analysis of the data. It can also be architected in distributedmanner such that some or all of the functionality is performed on addictand/or support network devices. It can be architected such that some orall of the functionality is done in the Cloud via various forms of cloudcomputing. Regardless of physical and/or logical distribution offunctionality, it may be described as or referred to as a server unlessotherwise indicated.

The server serves as a monitoring, assessing, and controlling functionof, for, and/or on behalf of the addict. This could include providing avariety of alerts to various resources that the addict is in a high-risksituation or area. This control could further extend to actions such asdisabling the addict's car, informing the addict's addiction sponsor orcommunity members, or alerting family or law enforcement about dangeroussituations. Indeed, one example embodiment of the present disclosureprovides a form of involuntary monitoring and action coordination, notunlike GPS ankle bracelet monitoring, where an addict on parole foraddiction-related offenses (e.g. DUIs, etc.) may have their devices (andeven attached sensors such as Blood Alcohol Content sensors) monitoredto detect the presence of offending substances, and implementing actionsto mitigate the risk to the community and the addict themselves.

Another aspect of exemplary embodiments of the present disclosure is theuse of multiple location determination technologies or sources 101 todetermine locations of addicts and other persons/places/things. Thesetechnologies or sources 101 include, but are not limited to, sensornetworks (e.g., Internet of Things (IoT) 101 a, etc.), GPS/Assisted GPS101 b, cell tower identification 101 g, cell tower triangulation (TDOA,AFLT), beacons 101 c, Radio Frequency fingerprinting, Real-Time LocationServices (RTLS) 101 e, WiFi based location systems 101 f, RadioFrequency Identification (RFID) based location systems and similarsystems, drones 101 d, crowdsourcing, hybrids 101 i, simultaneouslocalization and mapping (SLAM) 101 h, and/or combinations of these orother location determination systems. These location determinationsystems may be on, worn or carried by, used by, embedded in, or nearbythe addict or addiction-related resource sufficiently to determineapproximate location.

Not all aspects of the present disclosure need to be centralized in theaddiction server. The addict's local device(s) 115 a may also havefunctionality as disclosed herein, both for Peer-to-Peer, IoT, Mesh,ZigBee, LPWAN, Star, Client/Server, and/or machine-to-machine (M2M)networking, situations and in circumstances where the addiction serveror other parts of the present disclosure are not operating oraccessible. An example of this functionality is in the deviceon/in/around the addict detecting a high-risk situation and the addictattempting to enter and drive a car in an underground garage (therebypreventing a GPS locate). The addict's device would automaticallyconnect with the vehicle's transportation system 119 (e.g., personalvehicle, friend or colleague's vehicle, transportation service likeUber, airlines, public transportions, etc.) to inform or provide analert of a high-risk situation and proceeding to disable the car.Indeed, many, even all of the server's functions could conceivably bedone in one or more of the addict's device(s) or in other computing/dataprocessing architectures such as cloud computing; a centralized serveris a convenient/logical way to represent many of the presentdisclosure's functions, but not inherently necessary to its overallfunctionality. For example, the risk/prediction engine part of theserver could easily be resident on the addict's device(s)(client).Indeed, in one exemplary embodiment, many or even all functions could beresident and/or controlled on the client.

Similarly, not all devices that can be used are depicted in FIG. 1.Devices 115 a that can be associated with the addict include but are notlimited to portable devices such as mobile phones/smartphones, tablets,laptops, other portable or mobile devices, etc.; wearable devices andtags on or in clothing, jewelry, shoes, watches, etc.; mobile paymentdevices/wallets, etc.; embedded sensors, tags, chips or otherelectronics that can be implanted or ingested (e.g., ingestibles orimplantables, etc.) in an addict, augmented reality andheads-up-displays (e.g. Google Glass, etc.) and virtual reality-enablingsystems. Fixed or mobile/fixed hybrid devices 115 b such as desktopcomputers and smart home connected devices that can also be associatedwith the identity and/or location addict are also part of aspects ofsome exemplary embodiments of the present disclosure. For example, FIG.1 shows additional examples of smart home connected devices 115 bincluding a TV, refrigerator, and microwave. As more and more devicesbecome smart, the smart device will have the ability to capture datathat will help determine a person's location/context through onboard orconnected data capture devices such as video, audio, and/or othersensors. Combined with the device's known location (or ability todetermine the device's location), and the connectivity associated withcommunicating to and from these devices (also known as the Internet ofThings or IoT), these devices/networks may provide new key sources ofpersonal context information.

FIG. 1 also discloses a variety of location and categories useful in awide variety of embodiments, not limited to the addict, supportresources, or even enforcement resources. One key concept can be toutilize these varieties of location types and categories in a variety ofways in supporting the above resources. These include, but are notlimited to:

-   -   Base Locations 121 including common locations for the addict,        such as home, work, school, or church, etc.;    -   Frequent Locations 122 including locations frequented often by        the addict, such as homes of friends or family, stores,        restaurants, malls, gym, hobbies, etc.;    -   High Risk Areas, Events, Locations 123, such as liquor stores,        casinos, concerts, drug-dealing areas that could pose a        temptation for the addict, bad peers, trigger activating        situations, etc.    -   Sanctuary Areas, Events, Locations 124 where an addict might        feel safe and have a very low temptation to use, such as AA        meetings, churches, key/safe friends and family members, dry        public areas or events;    -   Virtual Locations 125 including online forums, such as Facebook,        Twitter, Women for Sobriety, therapy sites/sessions, Virtual        Reality Locations, addiction communities where an addict can        involve him or herself safely (without using) in an online        activity. This includes the use of virtual and/or augmented        reality to put oneself in a different (safer) location, context,        and/or frame-of-mind.

While location is most often a key distinguishing characteristic,context can also be important, particularly for support network members.Just having a support person nearby in time of trouble is not enough,the person needs to be available, interested, and in a position (e.g.situation/context) that he/she can break away from whatever they aredoing to help the addict. Thus context-determining sensors and othermechanisms can be important not only to the addict but the supportnetwork as well.

Put another way, Base Locations are where the addict is frequently, suchas Home, Work, or School. Frequent Locations are where the addictfrequently visits such as family and friends, the addict's gym,frequently visited stores or restaurants, and various hobby locationssuch as a bowling alley. High-Risk Locations are another element used invarious embodiments, to track possible high-risk areas for the addictwith both fixed locations (such as liquor stores, casinos) and varyinglocations (such as recent drug-activity areas). Opposite these high-risklocations would be Sanctuary Locations, where the addict will presumablybe especially safe from high-risk situations, such as an AA meeting orchurch service. The use of Virtual Locations is also disclosed, usedwhen an addict is logged into and/or viewing a service like Facebook orTwitter or is using Virtual Reality devices such that the addict is onthose applications and can be contacted or otherwise influenced by thoseapplications.

These location categories have a wide range of uses. They can be anintegral part of action determination when relapse risk is high, byfinding the closest Sanctuary Location, for example, and arranging for anearby support person to meet the addict there, including providing eachof them directions via their navigation application based on theircurrent location. High-risk locations of course are to be avoided, butcan this can be done in many ways. They can be omitted from navigationapplications (de-augmented) to prevent temptations. Geo-fences can besetup around them such that when the addict enters one automatic alertsare sent to nearby support persons to give them a heads up to possiblyprepare for an interception. Indeed geo-fences can play an importantrole in various embodiments, e.g., in risk prediction (e.g. if ahigh-risk location geo-fence is violated then risk score goes up by 20%,etc.), support resource identification (alert people within 10 miles ofthe addict when X occurs), and context setting (e.g. locations withwalking distance of home-half a mile-are considered safe/home location),etc. If an addict is within 1000 feet/5 minutes of a park, then awalking/running excursion can be added to a list of potential actions.And so on. Virtual locations can also have a wide range of uses, e.g.special Reddit groups, as could quasi virtual/physical locations such assafe zones for Anxiety sufferers within the broadcasting distance of acoffee-shop beacon, that provides for virtual or physical meetings ofsufferers only within range of the beacon.

In various embodiments, a variety of sensors, devices, and mechanism,may be used to determine the location and particularly the context ofthe addict. FIG. 2, for example, describes a device 200 an addict mighthave on their person (e.g., a smartphone, etc.), wear (e.g. in the formof a watch), have implanted, or otherwise be on or near an addict. Thisdevice 200 could contain a variety of sensors 212, such as sensors thatdetect and capture sounds, images, video, or body conditions. forexample. FIG. 2 provides a detailed (but not exhaustive) list of suchsensors 212, including Blood Pressure sensors, Breathalyzers, BloodAlcohol Content sensors, Environmental/Weather sensors, Skin Temperaturesensors, Olfactory (smell) sensors, Bio/gestation sensors, Vestibularsensors, Kinesthetic sensors, and Sight/Vision/Optical sensors to name afew. Other sensors may also be used including sensors for WaterQuality/Pressure, Chemical/Gas/Fire/Smoke/CO2/Flood, Level, Gyroscope,(Passive) Infrared, Eddy current, contact, Ultrasonic, Images, Alarm,Doppler; Fiber Optic, Occupancy, Reed, Touch Switch, Magnetic,Inductive, Microwave, Radiation, Parking, etc.

The use of these various sensors can be to aid in the detection and/ordetermination of the addict's context, e.g., what the addict isexperiencing, feeling, even thinking, etc. Even further, to the extentpossible individually or in combination with other sensors and/or data,the goal of such sensor use can be to detect/determine what trigger(s)the addict may be experiencing, and to what degree, in order to gaugethe risk of relapse and factors involved in the potential relapse, andto identify and set in movement a course of actions that will preempt orprevent an addict's environment from deteriorating to the point ofrelapse.

FIG. 2 discloses one such device(s) 200 that can sense, monitor, and/orcontrol aspects of an addict's context. The device 200 includes an arrayof capabilities, including sensors for detecting or anticipatingaddiction trigger conditions (e.g., contexts, situations, circumstances,environments, and/or state of mind(s) that may cause the addict torelapse or use substances or activities related to his/her addiction,etc.); mechanisms for interfacing with the addict includingtangible/tactile Interfaces 201 (Display, Lights, Sound, Vibration,Heat, Smell, etc.). For example, a Smell Interface could generate thesmell of fresh pine trees or pine tar in response to high riskassociated with the Escape trigger being detected (helping remind theaddict of good times he has had when hiking in the Rocky Mountains). Thewide variety of interfaces is premised that dissemination of informationto/from an addict needs to be in the most effective means possible atany given time or context, which can vary from day-to-day orhour-to-hour. In addition to traditional interfaces such as sound orvibration interfaces, some exemplary embodiments of the presentdisclosure include the ability to project (or interface to a projector)2 or 3 dimensional images, video, GIFs, or real-time holographicprojections that the addict can converse with. It also includes theability to augment reality (insertion of images not actually present),and even (de) augmenting reality, such as the elimination of addictiontriggers/temptations such as liquor stores from the addict's vision.

FIG. 2 depicts a partial list of a variety of sensors that can be usedto determine the addict's context. These sensors can be usedindividually, in combination, and/or with other information about theaddict, environment, situation, circumstance to determine the addict'scontext, as well as determine if there are any triggers being activatedor in the process of doing so. Simple examples include using acousticsensors to detect the volumes and type of sounds in an addict'svicinity, to potentially identify triggers such as Noise (e.g., too highor low volume, etc.), Children (e.g., detecting high-pitched voicesand/or multiple children, etc.), or Social Situations (e.g., multiplevoices in the background, etc.).

The addict's device 200 shown in FIG. 2 may be configured to serve as alocal controller of information of, in, and around the addict. Towardsthat end it is, to the extent possible, self-reliant and contained inhow it collects, processes, and disseminates data-based andphysically-based actions. To begin with, it provides a set oftangible/tactile interfaces for interacting with the addict, under theassumption that having good preventative actions is only part of thebattle such actions must be presented in a manner acceptable/receptiveby the addict. Very often this is dependent on the context of theaddict; for example, at night an addict may be most receptive toaudio-based messages, while in the day he or she may be most receptiveto visual-based actions. In public places the addict may not wanteither, but instead be physically pulsed/shocked/vibrated/or heated toremind them they are increasing their risk of relapse for example (oneaddiction reality is that many relapses are not deliberate, meaning theyare a culmination of smaller, even innocent-seeming behaviors thatrapidly culminate in a relapse situation before the addict was evenaware they were in danger. Small shocks or other physical cues can helpwake up the addict early in the process and stop the danger before itstarts to build out-of-control).

The device 200 in FIG. 2 has a variety of capabilities to beself-sufficient and help monitor/manage/control the addict. The device200 has a Locator Unit 202 that has or interfaces to a variety oflocation determination technologies (e.g., GPS, Wi-Fi, CID, Beacon,TDOA, etc.). The device 200 has at least one CPU 203 and associatedmemory, storage, and calculating circuitry, hardware and software, to dodata collection, analysis, and decision making The device 200 hasspecialty processing and display/interfacing capabilities for managingsuch capabilities as virtual reality, (de)augmentation, holograms 204 a,device effects controller 204 b, and robotics 204 c. The device 200 hasa mechanism 210 for detecting and interfacing with other nearby devicesto extend its sensor/data collection capabilities (for example, tappinginto a nearby security camera to see the addict's surroundingenvironment). The device 200 has an onboard medical controller 207 thatcan interface/integrate with attached and/or embedded devices orphysical wraps, wearables, implants, and/or drug dispensers, in order toif needed suddenly inject or offer for immediate consumption anaddiction treatment drug. The device 200 has a User Interface detector208 that can tie into nearby systems if such systems are deemedadvantageous for delivering a message (for example, broadcasting themessage over a car's stereo system while it is playing a song (e.g.interrupting the song), instead of ineffectually playing on the addict'sphone). The device 200 has a digital assistant and/or interface to adigital assistant for managing the addict's day-to-day, hour-to-hourschedule. The device has a self-contained module for storing andmanaging the addict's triggers (particularly useful if the addict is cutoff from the broader system). The device has an onboard manager for allthe myriad of sensors and tags on or nearby the addict so such data canbe analyzed locally. Similarly, the device has a beaconinterface/manager 209 c to transmit/receive information from localbeacons. More broadly, the device 200 has the capability of determiningcontext locally, particularly in its ability 210 to tie into localnetworking connections such as Wi-Fi, RFID, RTLS, Bluetooth,peer-to-peer, Internet of Things, mesh/ZigBee, security systems, andother local-oriented networks. As shown in FIG. 2, the device 200 may beconfigured for connection and communication with companion technologies,such as imbedded/inserted/attached addict data/tag/devices 260, supportnetwork devices and/or applications 270, nearby cameras, videostreaming, beacons, sensors, context data sources 280 (e.g., cars,buildings, people, specialty information broadcasts, etc.), sensorarrays 290, Internet of Things (IoT) sensors, devices, and/or networks295 (also shown in FIG. 2b ), etc.

Other capabilities include an SOS button 211 or similar mechanism thatthe addict can push/activate when he or she is feeling particularlyvulnerable to relapse, which will in turn activate other portions,aspects or features disclosed herein. The device 200 also includesmechanisms for causing various levels of physical pain or discomfort(called a reinforcer 206) and/or pleasure, such as a sharp stingingsensation or warm/caressing sensation, that can be used to reinforce ordissuade certain behaviors, with the intention of preventing suchbehaviors and/or associating addiction-related behaviors or contextswith the pain or discomfort. The device also has a variety of identityverification/privacy protection mechanisms for protecting and if need bedisabling the device and preventing anyone from accessing the data onthe device. There are also user-controllable/definable capabilities onthe device, either programmable or insertable such as SIM-like add-onsensors.

The above does assume an all-in-one device for such capabilities. Suchcapabilities could be spread across multiple devices on and/or near theaddict. Example of these device extensions are specializedaddiction-related wearable and implantable devices that focusextensively or exclusively on detecting and reporting certainaddiction-related conditions, not unlike today's Fitbit, such asbracelets that do double duty as a fashion accessory and blood pressureand skin temperature monitor. These readings could be prompted by and/orreceived by the Addict Monitor/Controller (AMC) shown in FIG. 2, or theextended device could do self-contained monitoring of those conditionsand alert the AMC when they reach an concerning level. Smartphones withembedded alcohol detection sensors is another example. Theseaddiction-specialty devices could continually monitor addiction-relatedconditions which could then send alerts and data (including locationdata) to the addiction server when conditions warrant. Such deviceextensions could include permanent devices not unlike today's paroleeGPS monitoring ankle bracelets. A similar form could allow such actionsas court-ordered alcohol use monitoring for DUI (Driving Under theInfluence) offenders, or even voluntary usage by persons committed tobeating their addiction but needing extra external discipline to achieveit.

In fact, the use in Parolee Tracking and Monitoring Systems is anexemplary embodiment of the present disclosure in at least two respects.The first addresses the limitation that current GPS-based anklebracelets have in tracking and monitoring parolees and other prisoners,and that is its limited effectiveness inside a building. GPS does notwork well inside buildings or in other contexts where there areobstructions between the bracelet and the GPS satellites, such as treesor metal containers, etc. Exemplary embodiments may utilize a variety oflocation-determination technologies and methods to monitor the locationof the parolee. It then goes further to monitor the behavior of theparolee.

Of course, the devices and associated sensors and capabilities are onlyeffective if they are actually powered on. It is an unfortunate fact ofaddiction life that many don't want to become sober, or at least feelthey can be so without any outside help. In any event, it is assumedthere will be the temptation to tamper with/otherwise disable suchmonitoring devices, and includes the ability to determine whichaddict-related devices are active (powered on and in the proximity ofthe addict) and which are the primary one(s) in use at an given placeand time, via monitoring of usage as well as determining which device(s)are in the best position/proximity to monitor the addict's behavior,risks, and actions. This is important as well in general contextdetermination, as people are trending towards having multiple devices,only some of which are actively on or near the person at any given time.In particular, it may be important to determine the primary device whendetermining what the best interface is for communicating with an addictat any given time.

The above capabilities can be packaged in a variety of form factors,ranging from being worn on the wrist or neck to glasses form to evenimplants. Form factors include but are not limited to: wrist/ankledevices, wearables, implantables (devices implanted/embedded in theskin/body), clothes, accessories, wallet, Google glasses, Snapspectacles, heads up display, augmented reality displays, inserts (e.g.ear), FOBs, key chains, are some of the form factors, Siri personalassistants (Built-In, Included, Add-on), smartphones, tablets, laptops,personal digital assistants, etc. The capabilities shown in FIG. 2 donot have to all be in one device, but can be spread across manydifferent devices even ones with no physical contact with the addict(such as security cameras, local RTLS beacons, etc.).

FIG. 2a provides examples of distributed sensor deployment, datacollection options, localized sensors, and localized networks that maybe used in exemplary embodiments. Form factor/sensor placement 1 mayinclude a hat or headband. Form factor/sensor placement 2 may includeglasses or a visor. Form factor/sensor placement 3 may include earplugs,earpieces, earrings, etc. Form factor/sensor placement 4 may includeimplants/inserts (e.g., teeth crown, pacemaker, ID chip, medicinedeployment, etc.). Form factor/sensor placement 5 may include anecklace, piercings, nose rings, tattoos, etc. Form factor/sensorplacement 6 may include a shirt, blouse, jacket, coat, etc. Formfactor/sensor placement 7 may include accessories (e.g., pens, pocketprotector, umbrella, cane, tools, touchpoints such as buttons and lightswitches, etc.). Form factor/sensor placement 8 may include a ring,bracelet, jewelry, etc. Form factor/sensor placement 9 may include apurse, briefcase, etc. Form factor/sensor placement 10 may include oneor more sewn-in sensors, etc. Form factor/sensor placement 11 mayinclude a zipper, belt, buttons, etc. Form factor/sensor placement 12may include pants, a skirt, etc. Form factor/sensor placement 13 mayinclude underwear (e.g., a bra, etc.). Form factor/sensor placement 4may include a wallet including payment methods, etc. Form factor/sensorplacement 15 may include shoes (e.g., a heel of a shoe, etc.). Formfactor/sensor placement 16 may include a smartphone, tablet, laptop,PDA, FOB, key chains, etc. Included in the form factor/sensor placementmay be the deployment/use of Artificial Intelligence (AI)assistants/Digital Personal Assistants such as Apple Siri, Amazon Alexa,etc. Sensors may be included/attached in such assistants. Conversely, AIassistance functionality may be embedded/attached to a variety ofsensors and/or sensor form factors.

Also shown in FIG. 2a are localized Sensors, information stores,readers, transmitters, broadcasters (e.g., individual rooms, nearbypersons, environmental sensors, etc.), Internet of Things (IoT) sensors,devices, and/or networks. FIG. 2a further shows localized networks thatmay collect/disseminate local contextual information (e.g., multiplerooms, malls, campuses, stores, schools, office buildings, etc.),Internet of Things (IoT) sensors, devices, and/or networks.

FIG. 2b provides examples of internet of things (IoT) addict-relatedsensors, devices, and networks 295 that may be used in exemplaryembodiments. As shown, the IoT addict-related sensors, devices, andnetworks 295 may include smart home sensors, devices, and networks, suchas home controllers, window/door, garage doors, HVAC, lighting, kitchen,security systems, appliances, computers and media, furniture,furnishings, media, landscaping, TV, decorations, rooms, pets, traps,fireplaces, etc.

The IoT addict-related sensors, devices, and networks 295 may includesmart vehicle, connected vehicle, driverless vehicle sensors, devices,and networks, such as cars, trucks, aircraft, trains, boats, RVs/recvehicles, etc. addict/support resources are using, in, within, ornear—sensors, devices, and networks that provide location/context usageof such vehicles and directly or by inference usage and mindset ofaddict and/or support resources and activities to help detect,anticipate, prevent or mitigate high relapse risk situations, such asdeactivating manual driving capabilities and activating driverlesscapabilities (e.g. for drunk drivers, etc.).

The IoT addict-related sensors, devices, and networks 295 may includenearby human sensors, devices, and networks, such as nearby (to theaddict and/or support resource) person(s), devices, networks andsensors—including proximity and/or access to person(s) et al. andcontextual data on, in or near that person as well as groups of personsand activities to help detect, anticipate, prevent or mitigate highrelapse risk situations.

The IoT addict-related sensors, devices, and networks 295 may includesmart retail/activity sensors, devices, and networks, such asrestaurants, stores, banks/ATMs, arenas, gas stations, gym, parking,amusement, hospital, gym, etc.—places persons (addict and/or supportresources) might shop or otherwise spend time in, including indicatorsof items being considered, purchased, and/or used (e.g. liquor, etc.)and activities to help detect, anticipate, prevent or mitigate highrelapse risk situations.

The IoT addict-related sensors, devices, and networks 295 may includesmart office, work environment sensors, devices, and networks, such astemperature, entry/exit, security, work-activity related, stress (mentalor physical)-related, productivity-related, co-worker, office/workarea-related (e.g., conference room lighting, temp, A/C & Heating,Vending, Smoking machines, energy savings, bathroom, security cameras,lights, etc.).

The IoT addict-related sensors, devices, and networks 295 may includesmart city sensors, devices, and networks, such as public spaces andinfrastructure with associated sensors, devices, and/or networks (e.g.,that addict/support resources, etc.) including parking, meters,advertising, police, first responders, etc.) that are in proximity of,connected to, and/or associated with that provide location/contextualinformation about addict, support resources, and activities to helpdetect, anticipate, prevent or mitigate high relapse risk situations.

Some exemplary embodiments of the present disclosure may include orinvolve the collection of large volumes of addict-related data, which isto be handled in terms of volume of data as well as the protection andsecurity of that data and in particular the identity of the addict. Thesecurity and privacy of this data is protected in various embodiments,as the potential for abuse is huge given that in some instances anaddict's location and context may be tracked nearly 24/7 at times. Inaddition, the social and professional stigma of addiction remains hugein our society, and many—even most—addicts greatly prefer theiraffliction remain private. Accordingly, a variety of systems and methodsare provided for addressing such considerations, including but notlimited to limiting the length of time data is stored, physicallydistributing the data among different databases including havinglocation/super localized, context-specific, and/or trigger-specific datastores, invitation-only data access methods, time-limited networking,and encrypting and/or anonymizing such data so it is very difficult ifnot impossible to link addiction-related data to a specific addictidentity. Such protections may include unusual protection mechanismssuch as location selective availability coding (deliberately introducingerrors into location calculations) or “nuclear football” keys where thecodes for unlocking/decrypting data change daily and/or under physicalprotection (e.g. not stored where it can be hacked online).

In various embodiments, a learning engine is provided that utilizesartificial intelligence and other learning algorithms and methods tolearn from an addict's behavior and to refine various systems,algorithms, and processes, such as an addict's likelihood of relapse,effectiveness of actions taken, and types and frequency of datacollected. FIG. 3 depicts an example process for assessing an addict'srisk of relapse and determining potential actions and support resources,with FIG. 5 providing more detail on how such an assessment could occur,and how the assessment algorithm may be modified as more data pointsabout the addict's behavior become available.

By way of an example relating to an addict's Anger trigger, an initialapproach to detecting an Anger condition may initially be based solelyon blood pressure (e.g. a spike in blood pressure is indicative ofanger). However, learning mechanisms disclosed herein may find that fora particular addict the correlation between a spike in blood pressureand an anger condition is low. Instead, the mechanism may determine thatthe addict's anger is immediately preceded in a rapid rise in skintemperature, and is exacerbated when loud noises (e.g. yelling, etc.) isin the immediate vicinity. Thus, the addict's anger-related sensorreadings will change from monitoring blood pressure to monitoring skintemperature and noise levels.

Indeed, various embodiments provide leveraging of numerous data setsabout, related-to, or of potential value to the addict, and the use ofdata analytics, algorithms, and analysis engines to aggregate, compile,assess, analyze, synthesize, and otherwise bring together disparatepieces of information (many location-related) that can be used in theaddict's treatment. Data/data sets can include but are not limited toinformation regarding the addict's medical history, personal profile(e.g., friends, hobbies, etc.), schedule/calendar information,historical data (often location-based) that describes past actions andbehaviors, key enablers (people/places/things that can aggravate theaddiction), key usage triggers, and sources of addiction (e.g. liquorstores, drug dealers)/Points of Interest (e.g. bars, casinos) that theaddict has been known to frequent and/or has demonstrated vulnerabilityto in the past. Such information could be obtained in many ways,including but not limited to directly from: the addict (e.g.questionnaires, etc.), the addict's support network, friends and family,medical data, historical behavioral data, public records, news media,social media, school records, etc. The communications links used toobtain this information can include, but are not limited to, theInternet, wireless and wireline networks, cloud sources, crowdsources,peer-to-peer networking, sensor networks, machine-to-machine networking,smart homes/neighborhoods, and electrical-grid based networks.

Such data sets and other information may be analyzed on multiple levelsusing analytics that include but are not limited to a Usage Trigger,Potential Response Analyzer, Risk Prediction Algorithms, and anAction/Coordination Engine (many of these concepts are illustrated inFIG. 1 in the Addict Data and Analytics, and used implicitly orexplicitly in many of the Figures). The Usage Trigger and PotentialResponse Analyzer takes as inputs the addict's usage triggers (definedby techniques such as questionnaires or psycho-therapy), and theaddict's personal profile and schedule/calendar, as well as historicalinformation about the addict's movements, actions, behaviors, andhobbies, to develop a risk assessment and prediction algorithms aboutthe addict's vulnerabilities to future potential addiction-relatedsituations and develop a series of potential responses. Risk PredictionAlgorithms utilize information about the addict's currentsituation/location/context, addiction triggers, and historicalbehavioral data to develop a risk score, rating, or level (score) forthe addict. If the risk assessment score reaches or exceeds a threshold,and/or falls within a certain range, the Action Coordination Engine willdevelop an action or course of action that will then be launched, suchas contacting members of the addict's support network, rearranging theaddict's navigation (away from high risk locations), or disabling theaddict's vehicle and arranging for alternative transportation, as a fewexamples.

In various embodiments, an action/coordinating engine (based on theAddiction Server, one or more of the Addict's devices, via a cloud, orsome combination) is provided to coordinate various actions on behalf ofor in the interest of the addict. This action/coordination engine(action engine), the use of which is illustrated in several drawings inparticular FIGS. 6, 6A, 7, and 7A), is responsible for identifying,determining, and/or managing a variety of actions that the addict, theaddict's support network, or others can take on behalf of the addict,usually in response the detection of a relapse risk or actual relapsesituation. Such actions could be automatic in nature, such as the enginedirecting the addict to move to a sanctuary location for example, suchas a nearby AA meeting getting ready to start, in response to a highrisk alert generated in various embodiments. An action could be more ofa coordination function, such as coordinating a meeting between theaddict and a nearby addict sport person at a nearby meeting in an hour,action could include interfacing with the meeting place (e.g.restaurant) reservation system, as well as identifying nearby availableparking, and providing instructions to the addict's and the supportperson's cars to activate the self-parking application once in range.This is an example of communicating/coordinating with a variety ofthird-party applications and services that will make the action(s) go assmoothly and hassle/aggravation-free as possible, as a key to resolvinghigh-relapse risk situation without relapse is often to the addict in asa tranquil, trouble-free mindset as possible.

Other actions include obtaining alternative transportation for theaddict, modifying navigation applications to avoid areas whereaddictions can be enabled (such as liquor stores for an alcoholic),and/or posting on social media that the addict is in a high risksituation. Note not all actions may be reactive, or reactive only toonly high-risk or in-progress relapse situations. The action engine maymanage the selection and communications of daily morning motivationmessages to the addict, or perform an alert reminder function to one ofthe addict's support persons reminding him do to his weekly Friday callto the addict.

An often underappreciated and overlooked aspect of successful addictiontreatment is the use of rewards mechanisms associated with good ordesirable behavior. While addiction outsiders often take the attitudethat avoiding the destructive aspects of addiction should be motivationenough for an addict to get and stay sober, the reality is that for manyaddicts that is not enough. Indeed, the destructive aspects of addictionget progressively less of concern to many addicts the longer they areaddicted. However, the prospect of a reward for good behavior (notrelapsing) can have a very stimulating/powerful effect on someaddicts—thus, various embodiments provide a method and apparatus fordetecting/determining good behavior and rewarding such behavior. Forexample, as shown in FIG. 9, a rewards mechanism is provided under thepresumption that many addicts' need a positive-enforcement mechanism asa deterrent to relapse. For example, engaging in good behavior such asavoiding trigger aggravating locations and contexts earns points, asdoes attending (in person or virtually) addiction community meetings.The points accumulate, and can periodically be redeemed for (usually)addiction-related goods and services, such as passes to (non-alcoholserving) movie theaters or deep discount coupons for safe hobbies suchas gym memberships or sewing supplies. In contrast, risky behaviors suchas visiting with bad friends (friends that very actively drink forexample) could results in points being deducted.

More broadly, various embodiments include rewards for behavior thatinhibits or prevents addiction usage/relapse (e.g. good behavior),and/or behavior that specifically avoids relapse or the possibility ofrelapse (e.g. bad behavior). While ideally the addict will beself-motivating in his/her desire to get sober, the reality is that manyif not most addicts need some sort of external motivation andreinforcement—both positive and negative—to get and particularly staysober. Thus, a sobriety rewards program can be an integral part ofvarious embodiments. For example, the addict may accrue reward pointswhen he or she spends time in a new (good) hobby, or exercises for atleast 30 minutes a day. Similarly, points may be earned if the addictdoes not go near vulnerable locations such as high drug areas, badfriends, or liquor stores for at least 30 days. An example embodimentwould detect and track these good behaviors through functionalitydisclosed herein and/or data obtained from interfacing with otherapplications and devices used by the addict or his/her support network.Rewards could be in many forms, such as points redeemable for goods andservices, discounts on using an embodiment of the present disclosure orrelated applications, free tickets to an event (presumably but notnecessarily safe events), or even direct cash credits to their bankaccount or mobile pay account. Note a further feature of the presentdisclosure may be to detect/prevent the use of any financial transactionfor the purchase of substances negatively related to the addict'saddiction. Thus purchases from liquor stores would be detected andrejected via interfaces with the addict's financial accounts andtransaction enablers, e.g. credit/debit cards, mobile pay and bankaccounts, etc. Similarly credits could be posted to these accounts.Credits could be selectively posted for good behaviors, such as payingfor new hobbies, free Uber rides, etc. Similarly, bad (or not good)behavior could result in points/financial deductions from the addict. Inone example embodiment, a reward engine would determine theapplicability and value of such behaviors, track suchadditions/deductions, and coordinate with addict-related third partyaccounts.

Not all motivation-enhancers will be rewards-oriented; some need to beencouragement-oriented. This would include words-of-encouragement,testimonials, thoughts-for-the-day, and other types of motivation orself-esteem building messages. Thus, various embodiments provide ways ofdetecting when such messages would be particularly timely, selectingappropriate messages, and delivering them to the addict at theappropriate time, context, and using the best method for encouraging theaddict to get on/stay on the path to sobriety. For example, anembodiment of the present disclosure may determine that the addict issusceptible to the Time of Day trigger, when he/she particularly desiresdrinking at 5 pm every day. Starting at 4:30, motivational messages andtestimonials may be sent every 15 minutes, such as “life is short—makethe most of it!” This could be sent using any number of methods,including text, MMS, email, (pre-recorded) phone call,internet/(private) social media, and other methods. The embodiment couldalso automatically create rotating schedule reminders to (random orpreviously selected) various members of the addict's support network sothat those members could connect real-time with the addict to offerwords of encouragement during these vulnerable times-of-day.

Sometimes actions require more than a positivereinforcement-orientation, meaning a more urgent, aggressive approachmay be needed. One such scenario could involve the detecting of theaddict relapsing while at a bar, and having driven himself there. Theaddiction server will have monitored the addict's smartphone forexample, showing that both it and the addict's car have been followingthe same path and has stopped at a local bar. Sensors on the addict'sclothing will have detected the presence of alcohol, and other sensorsnoting a risk in blood pressure and blood alcohol content. Such data maycause the Risk/Prediction engine to generate a very high alert—addicthas been drinking. If no addict support person is in the immediatevicinity to come get the addict, the action engine may elevate theurgency and send signals to the addict's vehicle that disables it (analternative would be putting it into self-driving mode if such optionwas available). Concurrent with disabling the call would be requestingan Uber/taxi ride dispatched to the bar, with an alert to the addictthat such a ride has been arranged for, with the details showing on theaddict's smartphone screen. To make it clear that the addict needs toget into the taxi, the action engine may also inform the addict that oneof his support network will be meeting the taxi at his home in 30minutes, and he is expected to show at that time. Since this meetingwould be considered a penalty meeting, the addict will lose points fromhis sobriety program if he is not punctual.

Numerous communications methods to/from the addict and other resourcesare used in various embodiments of the present disclosure. These caninclude (but are not limited to) text/SMS/MMS, voice calls, email,social media, video, peer-to-peer and machine-to-machine communications,instant messaging, voice messaging/mail, 3rd party applications,heads-up-displays (such as Google Glass), hologram projections, andother applicable voice and data methods and mediums. For example, theserver may alert via text someone in the addict's support network thatthe addict is nearby and should be contacted because of a detection of ahigh-risk situation. The support person may send the addict an MMS withan uplifting message, and also send an invite to the addict via a 3rdparty application to meet him at a local restaurant at a certain time.The addict could respond with an instant message thanking the supportperson, and accept the invitation via the 3rd party app along with anote that the addict will be approximately 15 minutes late. The supportacknowledges this via a return text, but hits an option on his device toinstruct the addiction server to increase the location monitoring of theaddict to detect if he/she takes a relapse-type action (such as stoppingat a liquor store). These real-time updates may be transmitted to thesupport person in the support person's car system display/voice systemor augmented reality sunglasses on the way to the restaurant so he/sheknows exactly what the addict has been doing since the original alertwas issued.

As introduced above, a key element present in many embodiments of thepresent disclosure is the use of location technology such as mobiledevices and associated GPS or other location determination technology totrack or monitor the location of the addict. The addict's location canbe continually, periodically, occasionally, or on an ad-hoc basiscompared to numerous other individuals and/or data sources to providelocation-related assistance to the addict in continuing his treatment orin avoiding succumbing to the temptation of his or her addiction.

Another feature or aspect of the present disclosure is the use ofunaugmented reality technology. Instead of inserting objects into anaddict's environment (such as a Pokémon character), instead an exemplaryembodiment includes performing a continuous, real-time or otherwisetimely monitoring of the addict's environment, detecting threateningobjects such as advertisements for alcohol, and blanking out orotherwise hiding or obstructing the addict's ability to perceive suchthreatening objects. This could range from removing such objects from anavigation screen (such as liquor stores) to actual blanking orreplacing said object within a Google Glass or Snapchat Spectacles orother Virtual Reality interface.

Another feature or aspect of the present disclosure is the developmentand use of risk scores of relapse or other adverse treatment situation.Such scores could be in the form of a number (like credit scores), ahigh/medium/low designation, color schemes (e.g. Red, Yellow, Green,etc.), and other scoring and/or range classifications (scores). Suchrisk scores could be for a variety of risk types: general risks,situation-specific, location-related, and/or date/time ordate/time-range specific risks, among others. An embodiment woulddevelop these risk assessments/scores using data/data sets but notlimited to the addict's medical history, personal profile (friends,hobbies, etc.), schedule/calendar information, historical behavior data(often location-based) that describes past actions and behaviors, keyenablers (people/places/things that can aggravate the addiction), keyusage triggers as described above, and sources of addiction (e.g. liquorstores, drug dealers)/Points of Interest (e.g. bars, casinos) that theaddict has been known to frequent and/or has demonstrated vulnerabilityto in the past.

Such scores would be developed/calculated continuously, periodically,ad-hoc, or on-demand, as well as when certain individual conditions aredetected by various devices and/or combination of conditions, as well asdevice-independent conditions. An example of a device-independentcondition would if it were detected by external data sources that theaddict was facing a heavy traffic jam on his usual route home at theusual time. Knowing from his trigger profile that this situation couldactivate the Anger trigger (e.g. road rage), the Risk Assessment Enginewould calculate a high risk score. This score, in turn, may be used invarious other aspects of the present disclosure.

Taken together, various implementations of the present disclosure couldmonitor the location of people in an addict's support network—such associal workers or their sponsor in an addiction treatment program—tohave them alerted and ready to stand by to assist the addict if theaddict appears to be heading into a difficult situation, such asphysically meeting an alcoholic if they appear to be in danger ofentering a bar. To detect this kind of scenario, the addict's locationmay be compared to other data sources of potentially dangerous locationssuch as bars or liquor stores (for alcohol addiction), knowndrug-trafficking areas (for drug addiction), casinos (gamblingaddiction), shopping malls (shopping addiction), and so on. If itappears that the addict is possibly going to enter a dangerous situationor area, the nearest person (to either the addict or the place inquestion) in their support network can be alerted to intervene.

In various exemplary embodiments, integration and coordination areprovided with the addict's medical and psychological status andprognosis. Medical and psychologist/psychiatrists can embrace variousembodiments as providing ways to monitor real-time the progress of theiraddiction patents, as well as provide near real-time/real-timeadjustments to medications. For example, an embodiment, in monitoringaddict with an embedded naltrexone dispenser, may start detectinghigh-risk behavior. The example embodiment could inform the addict'spsychiatrist of this behavior, who then may decide that the medicinewill need to be increased in dosage. The embodiment would interface tothe service/application controlling the embedded naltrexone, e.g.,either remote or attached/embedded with the dispenser, increasing thedosage, all potentially in real or near-real time, or as otherwisedirected by the addict's medical professionals.

More broadly, this may entail obtaining or otherwise receiving access toan addict's treatment records, providing updates to those records, andinteracting with rehabilitation, therapy, and/or medical providers. Itmay also entail acquiring access to pharmaceutical/drug treatmentprescription information, and being able to administer through theaddict's controller device real-time modifications to drugs administeredthrough the controller.

Such an embodiment could also be used by rehab professionals, inmonitoring compliance of addicts under active treatment, during thoseperiods where the addict is in a halfway house or similar situation,where the addict is no longer being closely monitored by rehab personnelbut it is desired to see if the addict can manage by oneself in asomewhat controlled situation. The example embodiment may monitor theaddicts attached/embedded devices as well as other behavior to detectout-of-desired norm conditions or positive (“good”) behavior which willthen provide additional data towards release of the addict fromtreatment.

Some exemplary embodiments of the present disclosure include the use ofcrowd sourcing in generating and disseminating addiction-coping ideasand actions. For example, in FIG. 10 an addict enters a room where an“open” beacon is placed as part of a real-time location services (RTLS)network. In addition to providing location help, the beacon also servesas a repository for addiction-related ideas only for those people whoare “tuned” to that beacon, such as a special ID and/or application thatenables a one or two-way communication between the addict and thebeacon. This could be, for example, only for alcoholics, only foraddicts with the Anxiety trigger, and only accessible if the addict isin physical communications range with the beacon. The addict coulddownload ideas for dealing with Anxiety for example, including specificideas relevant for that specific location. Alternatively, the addictcould upload uplifting messages such as praising the locale for itscalming environment. It could even be used to identify and connectaddicts with similar triggers that are in range of the beacon at thattime, thus allowing ad hoc, spontaneous, unscheduled, or flashinteractions not otherwise possible or likely. In effect, this wouldenable very specialized, private ad-hoc meetings or linkups between twoor more addicts suffering from similar issues, in a safe, public yetprivate, anonymized environment

As noted before, helping the addict avoid relapse into their addictionis not necessarily voluntary. Several of the embodiments of the presentdisclosure are predicated on providing help to the addict without theiradvanced knowledge and/or permission. In various embodiments, noassumptions are made regarding the legality of such assistance, but itis presumed that the embodiment includes acquiring permissions from/forthe addict, either directly or via parental/court-ordered ones on behalfof the addict.

One example embodiment of an involuntary use is tracking of the mobiledevice(s) of an alcohol-related court offender. An extension of GPSdevice tracking for parolees, the embodiment could continually track themovements of a court-ordered person who is required to attend AAmeetings and/or stay away from any alcohol establishments. Theembodiment would correlate the person's movements and report back to thecourt or parole officer to confirm adherence to the court order, oralternatively provide proof of violation of the order. In extreme cases,an embodiment could be configured to report directly to the local policeany situation where addict impairment is detected, along with necessaryinformation to apprehend the addict, e.g. location of theaddict/vehicle.

Various embodiments provide insertion, to the extent possible andappropriate, of inspirations, testimonials, motivational messages, andother positive (or deterring) information into an addict's daily life.What messages/information this would include would depend on theaddict's location and/or context, in order to maximize theirappropriateness and effect. A common theme throughout many of theembodiments is the use of these kinds of messages. The “delivery” ofsuch can come in many forms—again geared to maximizing the ability forthe addict to pay attention to and assimilate such messages through theappropriate interfaces as well as third party applications such associal media, twitter, Facebook, snapchat, etc.

Various embodiments of the present disclosure provide monitoring of anaddict's physical and mental condition through the use of sensors, suchas wearable ones of even medical sensors embedded in the addict's body.These sensors can be monitored to detect conditions of particular dangeror vulnerability to the addict, such as a spike in blood pressure thatcould be indicative of the addict becoming angry. If this trigger isdetected, a message could be sent to the nearest person in the supportnetwork to alert them and have them contact the addict to arrange acalming meeting. Alternatively, if there is no one nearby, an embodimentcould then interface with other medical delivery systems attached orembedded in the addict to deliver a tranquilizer or dose of acraving-inhibiting drug or other medical treatment. Similar actionscould be taken with implants, prosthetics, or other artificialbody/brain parts.

Various embodiments of the present disclosure provide minimizing of theextent of and damage from a relapse of the addiction. This could includethe detection (such as via sensors, mobile device, or support network)that the addict has relapsed. This relapse once detected may set off achain of events such as locking or disabling the addict's personal orwork transportation if he/she gets within 100 feet of it; toautomatically call a taxi or Uber-type service if the addict is in needof transportation (facilitated by accessing the addict's schedule andrelapse contingency management instructions); to alert as appropriateportions of the addict's support network; alert family that the addicthas “used” and is under-the-influence (for substance addiction); ifappropriate automatically lock-out the addict from certainhouses/buildings if the addict comes within a certain range; to alertschool officials if the relapsed addict is detected with a certaindistance of their children's schools; even to alert law enforcement (andhis lawyers) if a breach of a restraining order is imminent

Indeed, there are many transportation-related embodiments related to thepresent disclosure. As numerous studies show that a high percentage ofviolent crimes (and of course other crimes such as drunk-driving) occurunder the influence of alcohol or other substances, there would be manyscenarios under which transportation would play a key role if impairmentof the addict is detected or suspected. These could include involvementof the addict's friends or family (e.g. automatic programing of adriverless car to take the addict to their house),ex-girlfriends/spouses (preventing driving to their location(s),addict's support networks (routing those persons to the addict), evenlaw enforcement (routing law enforcement to the addict's location).

As the above scenarios illustrate, the civil liberties, privacy, and/orsecurity of an addict are considerations to be taken into account invarious embodiments. Accordingly, in various embodiments, a privacyengine is provided that determines the conditions under which theaddict's location/context can or cannot be disclosed or used. In mostsituations, the privacy engine may be under the control of and/or entailthe acquisition of the approval of the addict. For example, the addictmay “pre-approve” an embodiment in which a car is disabled orre-programmed in the event that usage is suspected. Many addicts whoadmit they have an addiction and are trying to become sober will agreeto such conditions. They may recognize that that kind of monitoring andcontrol mechanisms would serve as effective deterrents. In variousembodiments, a privacy engine may prevent override of previousdeterrence approvals in contexts where the privacy engine detectsusage/abuse. But similar to parolee GPS ankle bracelets, the addict maynot have control over the privacy engine in some exemplary embodiments.Instead, it may be under the control of a parole officer, medicalprofessional, or other responsible party (for the latter, for example,the addict may sign power of control over to a trusted family member).

In various embodiments, various differing privacy levels and othercontrol mechanisms may be provided through multipleadministration/authorization levels. For example, an addict may have onelevel of access, and an administrator can have a second, higher-level ofaccess to control/override the addict's choices. There may also beadditional levels of admin/control such that someone such as paroleofficer or medical professional may manage multiple addict's profileswith the same user login information. Although the typical control overany one user's privacy may be controlled by only one individual at atime, it is conceivable that multiple persons may have concurrentcontrol, such as a parole officer and a medical professional.

There may also be the opportunity to anonymize people involved invarious embodiments. Not only may there be times when an addict desiresto be anonymous, even to people within his or her support network, but asupport network person may want to be anonymous to the addict. Anexample of the latter for example may be when there is no one availablein the addict's known network to talk to at a vulnerable timeparticularly if a relapse is in progress and no one in the addict'sknown network is available. In one example embodiment, an addict mayconnect, as a backup, to a general or on-call addiction support personthat may provide someone to talk to, or even arrange to meet if they areclose together.

In various embodiments, obstacles may be introduced to obtaining theaddiction substance or pursuing the addiction activity. One example isin using the information from one or more of the scenarios incombination with the proximity of the addict to transportation sources,such as the addict's car. In situations where the addict has relapsed,various embodiment components may automatically interface withtransportation systems within the addict's reach and/or control todisable them or modify them, for example switching on a driverless carfeature (and preventing manual driving).

In various embodiments, interfaces are provided that are ways ofinteracting with the addict. One common and unfortunately generallyapplicable characterization of addicts is the tendency to be lazy. It ismore accurate to say that because of the effects (or after-effects) oftheir abuse (particularly substance abuse) they have low levels ofenergy, motivation, and attention span. In various embodiments,interfacing i.e., receiving input from but particularly providing outputto the addict can be very effective when appropriately provided. Thus, awide variety of interfaces may be provided, the deployment of which isoften context-dependent, and appropriately simple and intuitive for theaddict to use, with minimal actions required on part of the addict.

The use of Siri and other types of personal assistants are anticipatedand included as possible interfaces. For example, if it the addict isdetermined to be at high risk of a relapse while home alone, a programcould be initiated that talks to the addict to ask the addict what isbothering the addict (if the trigger is Loneliness for example and nohuman support person is available), or to suggest a call to mom if atrigger response to do so is high on the response list. The use of suchinterfaces could even be adjusted to use the type of voice (e.g., male,female, English, Australian accent, etc.) that has been determined to beappropriate in the past. The use of the addict's children's voices couldalso be used, as a reinforcer or defense to not to drink otherwise thechildren will be harmed in some way.

The types of interfaces anticipated to be used are wide, varied andutilizing a diversity of technologies. They may range from, e.g.variations of smartphone interfaces (e.g., touchscreen, high qualityvideo/sound, etc.) to others such as augmented reality, personalrobotics, etc. The goal of using such interfaces in all cases is thesame: to have a significant effect on the addict, which in turn may beprovided by an interface appropriate for that addict in a particularcontext.

The use of augmented reality and robotics can be beneficial in addictiontreatment. With Augmented Reality, particular contexts/situations may bemodified to both see things that are not there, but not see things thatare there, or see different things (also referred to as de-augmentedreality). For example, for alcoholics who see a spike in their desire todrink when they see a liquor store, their Google Glasses or equivalentmay be programmed to block out all words and images of alcohol as theydrive by. An alternative may be to replace the words and images withsomething benign, such as words and images about a charity, or evenreplace them with disgusting words and images along site symbols ofalcohol in order to associate such disgusting words and images (such assomeone vomiting) with the concept of alcohol.

As discussed before, interfaces with third party applications may beprovided in various embodiments. For example, interfaces with socialmedia applications can identify friends who are nearby as candidates fortransactional support needs (e.g. serve as support network substitutesif no other resources are nearby). Navigation applications can bemodified to exclude the location of liquor stores or casinos.Communicating with support resources via Snapchat, WhatsApp, Twitter, orFacebook can be used depending on the best/most convenient way ofinterfacing with an addict and/or their support networks. A filter andprogram can be applied to online grocery applications that may preventalcohol purchases from being made. This type of prevention may beextended such that if an alcoholic nears a local bar, an interface maybe established with the bar's systems that download a Do-No-Serve noticeto the bar owner along with facial recognition information.

In various embodiments, a wide variety of interfaces may be provided tointeract with the addict, support network, and third parties. Suchinterfaces include but are not limited to: Direct manipulation interface(e.g. augmented/virtual reality), Graphical user interfaces, Web-baseduser interfaces Touchscreens, Command line interfaces (e.g., commandstring input), Touch user interfaces Hardware interfaces (e.g. knobs,buttons) Attentive user interfaces (e.g., that determine when tointerrupt a person), Batch interfaces, Conversational interfaces,Conversational interface agents (e.g. animated person, robot, dancingpaper clip), Crossing-based interfaces (e.g., crossing boundaries versuspointing), Gesture interfaces (e.g. hand gestures, etc.) Holographicuser interfaces, Intelligent user interfaces (e.g., human to machine andvice versa), Motion tracking interfaces, Multi-screen interfaces,Non-command user interfaces (e.g., infer user attention),Object-oriented user interfaces (e.g., to manipulate simulated objects),Reflexive user interfaces (e.g., achieves system changes), Searchinterface, Tangible user interfaces (e.g., touch), Task-focusedinterfaces (e.g., focused on tasks, not files), Text-based userinterfaces, Voice user interfaces, Natural-language interfaces.Zero-input (e.g., sensor-based) interfaces Zooming (e.g., varying-levelsof scale) user interfaces. Various mechanisms may be provided forselecting/modifying the interfaces based on the user's context. Suchmechanisms are part of the User Interface Detector/Selector/Interface(UIDSI) unit in the Addict Monitor/Controller (AMC) 200 shown in FIG. 2.

In various embodiments, robots and robotics may be used. A robot couldbe used, for example, to serve as an addiction monitor, controller,and/or enforcer. A robot, sensing or being instructed that ahigh-relapse risk situation is developing, might literally grab theaddict by the hand and lead them to a different (safe) destination.

In various embodiments, scheduling and to-do lists of the addict areutilized, as well as the addict's support network. For the supportnetwork, integrating with any scheduling program that they use (such asOutlook) can assist in determining their availability (and in someinstances location) when an addict risk situation occurs. For theaddict, an embodiment may make a dynamic scheduling adjustment and/oradd or delete to-do items if in so doing so reduces the risk of relapsefor a particular situation. For example, various sensors and otherinformation may indicate that the addict's anxiety levels are rising inthe morning. A schedule may have some high probably highanxiety-inducing appointments in the afternoon, for example a meetingwith an ex-spouse and their lawyers that afternoon. Various embodimentalgorithms may determine that such a meeting is too high risk, andprompt (say via a speech, Sin-like interface) the addict to determine ifthe meeting should be rescheduled, and do so if the answer is yes (insome embodiments it may even be done automatically).

Consistent with simple and intuitive philosophy of the interfaces, anAddict Monitor/Controller (AMC) 200 (FIG. 2) may have a variety of formfactors. Such form factors may include, e.g., being part of a mobilephone, tablet, PDA, or laptop; part or fully of Tablet/Laptop/PDA,implants, wearables, wrist devices, or any number of form factors. Notealso that while the functionality described for the AddictMonitor/Controller is anticipated to be done on the device, it is notnecessary, and could be done on a server, in the cloud, viapeer-to-peer, or some other processing mechanism.

The variety and diversity of interfaces and the ability to detect/selectthem including specialized interfaces within the Monitor/Controller andinterfacing to other third party devices via their interfaces—areeffective for: 1) determining the context, 2) receiving input from theaddict in the ways most convenient to the addict, 3) providinginformation/output to the addict in the ways most receptive to theaddict, and/or 4) providing the most effective deterrent(s) to prevent(or inhibit) a relapse from occurring.

For determining context, an addict user interface detector/selector(UIDSI) on the addict monitor/controller (AMC) may utilize sensors onthe AMC to detect context-related data. A simple example is using anoptical sensor to detect the addict's light environment, helping toindicate if the addict is inside or out, in a lighted environment ordark. Readings (like loud music) from the audio sensor may be used,e.g., to confirm/modify a conclusion (supported by data from theLocator) that the addict has entered a Rave (an impromptu party withlarge amounts of substances to abuse) and is in a high-risk context.

Various embodiments may include the linking and coordinating of ad-hoc,spontaneous, unscheduled, or flash meetings between two or moreaddicts—who may or may not know each other—who either are a) generallyopen to idea of meeting; b) would like to meet a certain time and/orplace; and/or c) are concurrent/coincidently sharing similar triggerrisk profiles and where a potential solution for a high risk situationis their meeting, preferably in a sanctuary location.

Various embodiments utilize physical locations as part of their riskidentification and/or resulting actions/solutions or coordinationefforts. However, many if not most of such embodiments may also usevirtual locations as part of their description. For example, if anaddict is posting on or in a chat room online (in a situation wheretheir physical location is not relevant, determinable, or near anyoneelse), and making comments indicating a relapse/usage mindset, a riskassessment engine through a social media monitoring module could detectthese comments and generate an alert to the addict's support network. Inturn, those members of the support network who were also on that socialmedia site at the same time (or could quickly log on to it) could thenjoin the addict at that virtual location to interact live with theaddict, to talk about their risk situation and/or active triggers. Inthis sense, the physical location of the addict and the appropriatesupport network person is not nearly as relevant as is their presenceat/on the same virtual location on the Internet that enables them tointeract real-time.

Various embodiments utilize algorithms that assess and determine thepotential of a relapse of the addiction(s) in question. Such algorithmsmay be based on many factors, including, e.g., addict's profile,behavior/context history, triggers, medical data, the current ortrending context of the user, etc. As shown in FIG. 5, such informationcould be utilized in a set of prediction algorithms and/or scoringformulas geared to determining a current or trending degree of risk.

Various embodiments utilize the concept of trending context of theaddict. Various embodiments may attempt to anticipate/predict theaddict's context at some point(s) in the future—10 minutes, 1 hour, 3hours, etc.—in order to identify high-risk situations and proscribe somesort of preventive actions. A simple example is detecting the travel ofthe addict on a route for which a highly likely/only conceivabledestination is a prohibited or forbidden destination, such as a liquorstore, gambling establishment, known drug-dealing area, etc., or relatedforbidden areas such as an ex-wife' s house under a court order ofprotection.

The above trending context example is also an example of variousembodiments' integration with other applications. In the above example,the prediction of the addict's destination of a liquor store, gamblingden, or drug haven may be made very simple if the addict programs thedestination into his/her GPS navigation application. An interface may beprovided by one embodiment that would automatically (subject to privacyand/administration limitations discussed earlier) transmit thedestination/route information from the navigation application to theembodiment, allowing confirmation of the addict's destination.

Various embodiments provide integration with third party applications,systems, and processes, e.g., as relating to dealing with high risk oractual relapse situations. For example, in situations where it is deemedtoo risky for an addict to drive a car, an embodiment may automaticallyscan through alternatives to get the addict home or to help, withoutthem endangering themselves or others by getting behind the wheel. Thiscould include automatically integrating with an Uber or Lyft applicationto obtain a ride, or interacting with the addict's car to alternativelydisable it, make it switch to self-driving mode (and not allow manualdriving at all), or only switch on if the addict was a passenger. Suchthird party integration could be extended even further, for example,extending to home security applications (e.g., sending an alert to anex-spouse's house under a restraining order to inform that the ex-spousehas been drinking and take appropriate precautions). It could evenextend, e.g., to automatically informing law enforcement if suchrestraining order geo-fences have been violated.

While the above embodiments emphasize anticipation and prevention ofrelapse, there are also many embodiments of the present disclosure thataddress minimizing damage if usage of the addiction occurs. In oneexample embodiment, alternative transportation mechanisms are leveragedwhen a risk of the addict traveling while impaired is considered high.Beyond and including communicating with the addict's personal vehicle(s)and interfacing with the appropriate systems to prevent the addict fromdriving the vehicle, the example embodiment may also interface withsystems such as Uber or Lyft to automatically request ride services whenit detects that the addict is in need of such transportation. It mayalso interface with driverless cars or cars with that option. Forexample, if the addict's personal vehicle has a driverless option, theexample embodiment may automatically switch the car into that mode toprevent the addict from taking control. An alternative may be tocompletely disable the addict's car, or any other car the addictattempts to drive (such as those with embedded breathalyzers or similardriver condition monitoring/car interfacing technologies).

Other transportation-related embodiments include, but are not limitedto, communicating with rental car companies if an embodiment detectsimpairment; airlines to prevent drinks from being served to the addict.It also includes alerting key parties (such as spouses) when the(possibly impaired) addict is on the way to home, and activate ifdesired various security measures when the addict is with a certaindistance of the home or other location (such as children schools) sothat measures such as automatically locking the home, callingsecurity/police, family etc. can be taken before the addict arrives atthe location.

Another example embodiment of the present disclosure providesinterfacing with the increasingly personalized digital media world.Digital signs/signage may be capable of communicating with personaldevices to detect information about the owner in order to tailoradvertising to the owner's specific needs, preferences, and desires.Other signage looks to market to common denominators with beercommercials and the like causing distress in some alcoholics. Oneexample embodiment may interface with public digital signage, e.g., toprevent alcoholism-related signs from appearing while the alcoholic isin the viewing vicinity, or alternatively causing uplifting,trigger-soothing related ads or messages to be displayed. The signagefor use by interfaces could be coordinated, e.g., by the AddictionServer or by an application on one of the addict's devices with accessto the Action Coordination Engine.

Various embodiments may address the privacy of the addict and others viaa privacy engine 117 to both protect the addict's privacy to the extentpossible and/or desirable as well as the privacy of others involved inimplementing such embodiments, such as friends and service providers. Asthe transportation embodiment above illustrates, implementing restraintson an addict in terms of utilizing third party services and/or informingthird parties of an addict's risk situation can involve privacy concernsand legal considerations. In various embodiments, a privacy engine 117may address such matters while still providing functionality asdisclosed herein. The privacy engine 117 may interact with variousembodiments such as the Action Coordination engine to ensure thatprivacy concerns are appropriately addressed in the selection andimplementation of any course of action.

One example embodiment includes sending alerts to persons in an addict'ssupport network when the addict is near, nearing, and/or stopped at aPoint of Interest—POI—(e.g. liquor store, casino, shopping mall, etc.)and/or known geographical area (e.g. drug dealing areas and sites) thatmay provide a temptation to the addict or indeed is the intendeddestination of the addict. Selection of recipients of such alerts may bebased on any number of parameters such as their location (e.g.,proximity to the addict), time of day, day of week, etc. The triggeringof such alerts could incorporate the use of geo-fences to initiate theapplication(s) and/or trigger various types of functionality when theaddict enters or leaves the geo-fence. This could help the addict inknowing he/she is being monitored (e.g. is being systematicallysupported by his/her personal, semi-anonymous, and/or anonymous safetynetwork(s), etc.) and/or causing members of the network to activelyintervene to help stop/prevent/advise/console the relapse or repetitionof the addiction event. This could include generating alerts when theaddict is near/nearing, and/or stopped by a person of interest, e.g. aknown bad influence on the addict (e.g., see FIG. 1, etc.), etc.

Another embodiment includes providing alternative routes for navigationapplications (and/or data to navigation applications that can providealternative routes) that modify directions to avoid these kinds ofproximity to the points of interests (POIs) or areas described above andother functionality described below (e.g., see FIG. 1, etc.).

Another example embodiment includes providing information/alerts toaddiction means entities such as casinos and liquor stores when thelocation of the addict is in their proximity, so as to enable on-siteactivities such as refusal of service or counseling to the addict.Various embodiments may provide information to credit/debit cardcompanies to temporarily disable the cards to prevent purchases or cashadvances, and/or disable the usage of other electronic or computerizedpayment mechanisms such as mobile wallet technologies. Variousembodiments may provide capabilities to block individual transactionswhen a part of that transaction includes addiction-enabling products orservices. For example, grocery stores in many states allow the sale ofbeer, wine, and even liquor. It would be very undesirable to block agrocery store transaction by the addict just because the place he wasshopping in sold alcoholic beverages in addition to many othernon-alcoholic items. Various embodiments may provide a capability to tiethe store's check out mechanisms (e.g. bar code scanners and the like)to the mobile payment system as well as to the data profile of theaddict (see below) to detect when an addict (in this case an alcoholic)attempts to buy alcohol. An alert may be sent to the mobile paymentsystem or perhaps directly to the grocery store's check-out system todeny the transaction. The addict would perhaps be given the opportunityto remove the alcohol from the checkout process and continue thecheckout without the alcohol being included to avoid embarrassment andthe like.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an addict with a panic button or hot-keyor equivalent on the addict's device(s) that could trigger somepredefined functionality described herein. Such activated functionalitycould also be based on the location, medical state, and/or othercondition of the addict, or even randomly selected.

Various exemplary embodiments may provide ability for an addict to postblogs on addiction websites describing his/her state of mind and/orother pieces of context, including location, that could help the addictin unburdening themselves and/or cause other addicts to post responseblogs and/or contact the addict directly to support the addict. Variousembodiments may provide linkages to book/article passages or videofootage showing the author(s)/actors in addiction situations and/ordescribing the impact of their addiction and/or showing them inembarrassing situations and the like. Various embodiments may providevarious media reminders of public figures who have seriously damagedthemselves in some fashion, such as high profile actors or sportsfigures who seriously impacted their careers by behavior that seriouslydamaged their public profile and in turn their careers and finances.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing predictive analysis of potential relapsebased on the addict's historical data, such as travel patterns andhealth data such as blood pressure, and linking them to real-time data(e.g. current location and directions; blood pressure monitors on or inthe person), to anticipate and evaluate the potential for a relapse, andto initiate preventative measures, such as some of the types of alertsabove or alternatives below, or even triggering a release of bloodpressure medicine or other types of medicine that would be deemedappropriate in the circumstances, such as Campral or Naltrexone (e.g.alcohol containing medications, etc.). These medicines' administrationcould be, e.g., in the form of alerts to the addict to take it urgentlyor as a reminder to a periodic schedule, or even triggering the releaseof the appropriate dosage via implants in the addict, or even moreexotic methods such as being shot with the medicine from a dronefollowing the addict, or providing a supercharged vibrate on the mobiledevice that could serve as a sort of wake up call, or more benignmechanisms to the addict like changing their ring tone to the addictsfavorite (or least favorite song), or launching a song on their iPodthat reminds them of good/bad childhood experiences. Alerts could besent, in various embodiments, to appropriate physicians and/or asupdates to the addict's medical records. Various embodiments may providecapabilities such as functionality during the addict's sleeping periodto subconsciously reinforce the addict's resolve to fight the addiction,such as special programs broadcast next to the addict's bed.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to sensors such as in-vehiclebreathalyzers that can activate alerts and other functionality disclosedherein. Various embodiments may provide capabilities such asaddiction-trained dogs that detect the presence/usage of the means ofaddiction/relapse and take preventative action such as hiding the means(a kind of reverse application of the dog getting a beer for hismaster). Various embodiments may provide capabilities such as affixingRFID tags on addiction means (e.g. alcohol) that trigger alerts andother disclosed functionality if the tags are moved.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing location-based alternatives via theaddict's mobile device to the addict to help either passively oractively in dealing with the addiction in general and/or relapses inparticular. Such alternatives include:

-   -   Alerting the addict when he/she is in the proximity of their        support structure, such being near an active or soon-to-start AA        meeting or other support groups that meets the needs/preferences        of the addict (example: a women-only meeting), or near a member        of his support network such as a sponsor, family member, or        friend; linking to navigation functionality to provide the        addict with directions to the support person or structure;        automatically calling the nearest support person or type of        support person (e.g. family member, etc.).    -   Providing location-based suggestions for other alternative        activities by linking with a personal preference database, such        as a nearby teen/youth center, church, movie theatre with a        movie/movie type he likes that will start soon, bingo, a bowling        alley, book store, coffee shop, and other types of alternative        activities based on a database of preferences, such as        self-esteem boosting activities which may include        volunteering/community service. Other alternatives may include        errand-running based on location, such as going to the grocery        store or picking up the dry cleaning. These could be done in        general while the addict's device is on and/or based on other        parameters such as only during a certain day or time or        geographical location, on a periodic basis, or randomly.    -   Providing location-based advertisements for key products and        services based in whole or in part on preferences and/or        templates for resisting temptation, such as bakery or coffee        coupons of nearby establishments (in this example, sugar and/or        coffee being considered an effective alternative to alcohol).        Could include special sales for that addict only and/or for a        limited time (e.g. 2 hours, etc.) on those products and        services.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes detecting that the addict, having been located athis/her residence for a certain period of time, leaves that residence ina way or timeframe that triggers an alert to said social network orlegal/public authorities. Said alert may preferably have the effect ofeither: 1) deterring the addict from said action (presupposing that thereason for the leaving of the residence is to indulge in the addiction);and/or 2) providing a public safety service.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages with databases storing images ofphotos, videos, text messages, legal transgressions (proven orotherwise), and other testimonial type data that can be used to remindthe addict of the consequences of their addiction, and/or becommunicated to their social safety network and/or other entities,particularly in the prospect of harm or inconvenience to otherindividuals and/or property is deemed possible. These linkages could beof a personal nature, or of an external nature, such as DUI car crasheshaving nothing to do personally with the addict but serving as starkreminders to the addict of the potential consequences of their actions.These images and/or messages could be automatically sent to the addict'sdevice and displayed in the most effect manner to gain the addict'sattention.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to/reminders of loss of marriage,deteriorated connections with children, family, and friends, loss ofjob(s), embarrassing behavior, situations or events caused by theaddict's addiction, negative financial impacts like lost opportunitiesor assets, and other personally negative situations experienced by theaddict. Reminders could be in any form, such as photos, videos, text,testimonials, etc., and rendered to the addict via display, text, audio,3D, 4D+, heads-up display, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to medical databases that showthe effect of addictions of various parts of the body, such as liver,stomach, colon, nose, and other effects such as blood clots, diabetes,etc. This information could/would be transmitted to the addict's devicein various methods, e.g. voice, text, photos, video, or other methodssuch as heads-up-display, direct neural-to-the-brain and/or other bodyconnections (e.g. implants or other methods of directly internallycommunicating with the addict, and other methods). This may includebeing able to display, text, and/or provide verbal reminders to theaddict and/or others in the addict's network. This may also include thepotential for notifications/linkages to other interested parties, suchas the addict's doctors, therapists, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to legal and other public recordsindicating personal or of an external nature of the effects of theaddiction, such as DUIs, restraining orders, and the like. This mayinclude being able to display, text, and/or provide verbal reminders tothe addict and/or others in the addict's network, particularly to thosein close proximity to the addict. This may also include potential fornotifications/linkages with public and private safety personnel andsystems in close proximity to the addict.

Another exemplary embodiment of the present disclosure includes thedevelopment and presentation via various types of user interfaces oflocation-based information about the above in the form of news, alerts,other presentations, etc. This presentation may be motivational and/ordissituational in nature to reinforce the need for the addict to abstainfrom their addiction. For example, a newsfeed could pop up as a bannerad on the addict Jane Doe's phone or Google Glass as she was nearing aliquor store on 5th and Elm Street saying “News Flash: Jane Doe laughsat the temptation of Joe's Liquor at 5th & Elm!—World Rejoices!” Thiswould have the dual effect of a) reminding Jane she is being watched ormonitored, and b) providing real-time reinforcement and praise forresisting that particularly temptation.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes the creation of an anonymous and/or semi-anonymoussocial networks, allowing users (helpers for the addiction that meetcertain parameters including location proximity to the addict) to flagthemselves as available to help (perhaps after achieving some sort oftraining) in the event that no one is available in the addict's personalsocial network, or as a supplement or substitute. These networks can becrowd-sourced from the community at large. The primary goal would be toempowering the addict to know that someone is just an action away if theaddict needs help. The helper could choose to appear anonymously or not,as could the addict.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes detecting, monitoring, and/or preventing purchasesand/or usage of addiction-related materials. This may include providingperiodic, continuous, or random reporting of such purchases and/orpurchase attempts to other interested parties on a specific,semi-anonymous, or anonymous basis.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing incentive programs and frameworks thatrewards the addict for positive behavior, such as coupons for freedinner for two if the addict's behavior reached a certain positivethreshold, such as not stopping at a liquor store for 30 daysconsecutively (verified by the application). This would entail ahistorical tracking/counter/points type system within the addict'sapplication profile. This would entail methods and technologies such asperiodic intensive tracking of the addict for an amount of time, such asa week or a month (or moderate tracking as a regular ongoing part of theapplication), and awarding points (redeemable for prizes) based onverifying going to a certain number of meetings or NOT stopping at aliquor store. This framework also has a lot of applicability for othersinvolved. There can be support quests, where say a child has to write aletter to their estranged parent (a real letter) and if so they get Xnumber of points, which are redeemable for products or servicesappropriate to that person's demographic, such as an ice cream couponfor children 10-15, or iTunes credit for persons 10-25, with appropriatecontrols to prevent abuse. Another aspect of incentives is abehavior-based pricing model. This could entail raising the monthly costof the service to the addict when the addict exhibits bad or undesirablebehavior (e.g., relapsing, etc.), then lowering the price and/orproviding refunds when the addict exhibits good or desirable behaviorover a certain period of time.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing a user interface (UI) platform to theabove functionality that minimizes or reduces the actions that theaddict has to take for the functionality to activate. Indeed, by usingcapabilities that the user does not have to provide any action toactivate the functionality, particularly the use of the addict'slocation, various embodiments may provide ways that reduce/eliminate anyreluctance on part of the addict to use the functionality. Variousembodiments may provide the ability to customize the user interface forthe addict to maximize the convenience and usability of thefunctionality based on the user's profile. For example, the UI for analcoholic may be much different than that for a prescription drug addictwhich in turn may be much different than the UI for a shopaholic. Theycould vary by many parameters, such as gender or location (e.g. text tovoice conversion could be done in a western, southern, or northeasterndialect depending on the home location of the addict, etc.). The sensorsused for detecting high risk situations may be much different; means ofcommunicating messages may be much different. Various embodiments mayprovide the ability to modify/customize the user interface based onmultiple parameters, particularly location. Other accessory typemodifications to a UI based on location also may be provided. Forexample, if an embodiment detects that an addict (or in this case alsoincludes non-addicts) is in a movie theater, it may automatically switchthe device to vibrate, and provide a capability that inverts the displayof any messages, e.g. instead of a bright background with messages indark colors, the background would switch to a dark one with the messagesin a lighter color, thereby greatly diminishing the impact on the darkmovie theater environment. This capability could be enabled by varioustechnologies tracking the movement and location of the person (addictionnot a requirement in this kind of use case), and other technologies suchas detecting payment for the product or service and using the nature ofthat transaction (e.g. buying a movie ticket, etc.) to automaticallymodify the behavior/user interface of the device. The device could thenbe instructed to revert to its former configuration when the user/deviceis detected to be leaving the theater.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an initial setup capability that minimizesactions and time by the addict to setup his/her profile to takeadvantage of the functionality described herein. A pre-populated profiletemplates may initially be used based, e.g., on a few parameters such asaddiction(s), gender, age, key medical conditions such as depression,medication taken, home/work locations, key hobbies/leisure activities,key addiction triggers, and/or (for some addictions) ethnicity, etc. Inaddition, various embodiments may provide the addict with a number ofoptions for setting up his/her support network. These could includemanual input of individuals/groups (generally undesirable),accessing/downloading individual/group information from otherapplications such as Facebook/social networking apps, phone and/or emailaddress books, local AA meeting participants, etc. The addict would thenneed to confirm each individual (using voice or computerizedconfirmation) before they would be active in the addict's profiledatabase, plus other parameters such as type of support (e.g. alert 24hours/day, only certain circumstances such as only when the addict isout of town, etc.). These individuals may need to be sent confirmationmessages, depending on circumstances (e.g. wanting to send alerts tothat user) and/or the addict's preferences, asking permission to beincluded in the addict's support structure. Other parameters such asallowing the use of anonymous support individuals/entities would berequested. This initial information would then be used to provide theinitial profile template for the addict. This information would includeflagging of location-related/addiction-related POIs, such as liquorstores within a 50-mile radius of the addict's home/work or movietheatres within a 10-mile radius. It would include key directionalinformation such as the various routes between home and work, whichcould then be used to provide alternative routes to avoid addictiontempting locations/POIs and triggers and/or provide convenient access toalternative activities, plus other data.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing an ongoing profile modificationcapability, which could be done by one or more means or ways: manuallyand incrementally, such as when the addict is calling, texting, oremailing a person asking the addict if he/she would like to add them totheir support structure. Various embodiments may also provide or have alearning capability, such as detecting changes in behavior or conditionof the addict (e.g., changed driving patterns or elevated bloodpressure, etc.), correlating the changes in the addict's behavior orcondition to relapse risk factors and then modifying theactivation/prioritization/type of functionality described herein.Various embodiments may include suggestions by others in the addict'ssupport network, such as recommending a good restaurant frequented byother addicts, a new bridge game being setup, a club at the addict'shigh school that might be helpful to the addict, etc.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing linkages to, usage of, and combination ofa wide variety of databases, including but not limited to:street/navigation databases; POI databases; satellite map databases;addiction group databases, such as AA meeting types, times, andlocations; event databases (e.g., ballgames, concerts, etc.); rehabfacility databases; self-help databases such as WebMD; weatherdatabases; public safety databases (e.g. police stations, sex offenderdatabases, etc.); geographical/terrain databases; highly specializeddatabases such as databases that show hot fishing or surfing locations;plus a host of individual databases such as local theaterdatabases/listings describing the locations, showings, and times of amovie theatre chain's location in the vicinity of the addict's home orwork. This may include linkages to products and services in the addict'sarea that could help deal with triggers particular to the addict. Thismay include sign-up databases for addicts in a certain area who want tobe part of a local, regional, national, and/or international (evenspecial) support network, on a personal, semi-anonymously, or anonymousbasis either offering to be part of a support structure, seeking thesupport of a support structure, or both. These databases could bepublic, private, or both. Various embodiments may provide new and uniquevalue both in the application/usage of the databases, and also incombining/integrating them in new and unique ways.

FIG. 15 depicts an example embodiment of a method for monitoring for arisk of a pre-identified behavior (e.g., pre-identified addict-relatedundesirable behavior, etc.). FIG. 15 also includes example triggers,priorities, and initial risk assessment/detection sensors. As shown inFIG. 15, a first step may include providing a questionnaire (e.g., onpaper, online, video, etc.). Questions on the question may be designedto obtain not just facts but to also elicit an emotional response fromthe person (e.g., addict, etc.) answering the questionnaire. Thequestionnaire may be used to determine the priority/impact/severity ofeach drinking trigger listed as well as any other ones that might apply.A high ranking for a trigger may indicate that the person often drinksor wants to drink when this trigger occurs, whereas a low ranking for atrigger may indicate that the trigger never causes the person to drinkor want to drink.

Continuing with FIG. 15, location(s)/context(s) for the addict may beextracted, such as from computers, phones, social media (includinglocation and time stamp), etc. Location(s)/context(s) for the addict maybe extracted from a third party, such as location, context-rich and/orimage/photo-intensive applications, e.g. navigation, etc. Afterextraction of location(s)/context(s) for the addict, the method mayinclude identifying, assessing, and prioritizing triggers, andidentifying key sensors, determining sensor and other sources (e.g., keyreadings, values, levels, ranges, yes-no parameters, etc.) to monitorfor risk. The method may also include accessing and/or using addict datasources including addict data and analytics 104 (FIG. 1), includingpredictive analytics data, etc. The various possible triggers shown inFIG. 15 are not necessarily independent from each other as there may berelationships between the triggers.

Another feature or aspect in some exemplary embodiments of the presentdisclosure includes providing the ability to anonymize the addict'sidentity for some or all of the above functionality, particularly (butnot exclusively) for functionality that goes outside of the addict'ssupport network, such as providing moment of value mobile couponsdiscussed above that would shield the addict's identity from the companyproviding the coupons.

More broadly, the protection of addict (and their support network)privacy is one aspect of the present disclosure. The personal datacollection mechanisms described in the present disclosure canconceivably be used to track a person's movements 24/7. To preventinappropriate use of data, the present disclosure provides new systems,methods, mechanisms, and techniques particular to how and where the datais collected, and who, how, when, and why it is used.

The present disclosure describes exemplary embodiments of newsystems/methods/techniques to use location and/or contextinformation-based security as a way to protect location and/or contextinformation. An important premise is that such personalizedlocation/context-based images and other prompting mechanisms are readilyfamiliar in some way to the user without the need of memorization. Atthe same time, such images/mechanisms would be very hard to hack orrecognizable by (ro)bots since the ability to recognize them is rootedin the user's experience—not in any sort of logical or algorithmicmechanism. Such security keys, passwords, or other security-relatedelements and mechanisms could be used in protecting the broadercollection of location/context information. Besides the ability toprotect the voluminous addict behavior data, such location-basedsecurity could be used as part of financial account passwordverification or reset processes, or even an extra layer of security toprevent individual household appliance or device hacking or access suchas preventing fake off hacking of TVs, etc. that could become morevulnerable as the Internet of Things becomes more prevalent.

One of the important premises of the location-based privacy and securitycomponents, embodiments, and examples is that location and context,presented in a user friendly manner, requires little memorization of theimage, unlike say an alphanumeric password. They are readilyidentifiable by the user once presented to them in some recognizablefashion. In particular, location/context can take advantage of theconcept in human memory of recognition versus recall. Recognition refersto our ability to recognize an event or piece of information as beingfamiliar, triggered or prompted from external piece of information orinput. Recall designates the retrieval of a piece of information frommemory without any external prompting or input. Today's passwordsrequiring number, letters, capital letters, and punctuation signs areexamples of account/data/database keys or passwords that rely in totalor significantly on a person's recall ability. Nearly all memory expertsacknowledge that recognition is far easier for most humans than recall.Location-based privacy and security takes advantage of recognition,which besides being easier for a given person it much harder for othersthat have not experienced the piece of data recognized by the user,hence it being much harder to hack, guess, or otherwise deduce,particularly if constrained in some way, such as a time limit, etc.

FIGS. 11, 12, 13, and 14 describe examples of location/context-basedelements and embodiments of protecting/verifying valid users and/oraccess to this information. Broadly, the capabilities illustrated bythese diagrams and associated disclosure and embodiments may be referredto as location-based security and/or location-based privacy, using someform of image(s) as key(s) to locking/unlocking/securing a broader setof information, such as account information and/or location/context datacollected for purposes of preventing or dealing with an addiction riskor relapse. The images are not limited to only visual/graphical items,such as photographics as the images may also or instead be visual,audio, graphical, video-based, photographic, textual, alphanumeric,and/or other types of passwords depending on the user interface. Theimages could be in the form of sensor readings, which often have uniquevalues depending on the sensor. The images could be multi-dimensional,such as two-dimensional (2D), three-dimensional (3D), four-dimensional(4D), or beyond if, e.g., including time/time-lapse/time-projectingimages. The images may be static (e.g. still pictures), dynamic (e.g.videos, etc.), past or present, based in memory or live streaming. Or,the images may also be combinations/hybrids of the above. In whateverform in various embodiments, the location-based security/privacy imagesare location and/or context-based, experienced by or known to the userand/or person(s) authorized to have access to the broader information.

Location/context images can be obtained, derived, or computed fromnumerous sources. FIGS. 1 and 2 show a variety of location/context datacollection devices, networks, sensors, and other mechanisms and sources.FIG. 11 further describes several example sources and associated typesof data along with examples. FIG. 11 also describes examples ofquestions that can be used to prompt for passwords/keys and/or accessto/resets/verifications of changes/access to key information. FIG. 11provides an example of a user memory profile that can help tailorquestions/potential answers to how that person best remembers/recognizesimages. Images may be a general term that is inclusive of all forms of Q& A methods and user interfaces. Questions can be tailored based on userinterfaces employed, e.g., text, photo, graphics, audio,Virtual/Augmented Reality, etc.

FIG. 12 provides more detail in terms of a method of how such keys mightbe obtained and used; examples of such keys; and algorithm examples forgenerating/developing/answering verification questions. FIG. 13 takesthe two-dimensional oriented algorithms and examples of FIG. 12 to threedimensions, showing how 3D images (e.g., Rubik's-cube type shape, etc.)could be used to introduce more sophistication intolocation/context-based security. FIG. 14 shows a variety of 3D formfactors and breaks out in more detail, data elements that would becaptured and associated with image keys in order to provide morealgorithmic and verification options.

For the figures, it is important to note the parts thatdevices/sensors/networks play in sourcing images, providing access toimages, and in algorithmic processing of such images. A 2D photographtaken by a low-resolution smart phone of a backyard swing set hassimilarities with and distinct differences from a 3D image capture orvideo of that same swing set taken by a 3D heads-up-display camera withmulti-media sound. The device, beyond capturing the time, place, andimage of the location/context, also adds certain contextual elements tothe image. In turn, those elements may play an important role in theability of the user to recognize the image later when it is presentedduring a verification/password acceptance Q & A process.

One of the embodiments illustrated in FIG. 11 is the concept of amultiple-key or jigsaw puzzle-based key or password forlocation/context-based security. The jigsaw puzzle based informationlock includes multiple keys to be correctly assembled in the correctorder/sequence in order to provide access to any data.

For example, the overall key is that a picture of a house must beassembled. This house has been broken up into 9 separate images with 9separate associated location/context-based keys. Each of the 9 keys werecreated and assigned during a particular time during which certaincontrollers or monitors were on-call for a particular addict.

The Location-Based Verification Examples sections of FIG. 11 describessome of these either as Verification Grid Element # or Grid #. Invarious embodiments, data produced and used as described in the presentdisclosure could be location/context images as actually experienced orseen by the device/user, or they could be representations of alocation/concept. Example of questions or limitations on verificationanswers are also shown in FIG. 11, such as “Where you have been in thelast (week, month, year, etc.)?” The use of multiple devices and/orperspectives of the same image (such as a house, daughter, etc.) wouldmake it much more difficult for a (ro)bot and/or hacker to replicatethrough algorithms, analysis, or even guessing, whereas for the user itwould take very little effort to recall/recognize the correct images. Inthe location/privacy-related examples and embodiments disclosed herein,a user may refer to a person or entity trying to gain access/validationto an account, database, data set, or other piece of information. Or,for example, a user may refer to a person and/or an entity generatingsuch data/information. A user can be a human, computerized entity,and/or anyone or anything that has valid permission to access theinformation involved.

In addition, there are potentially more persons/entities involved inlocation/context privacy and security than just users. Certainly datacan be generated/sourced from a wide variety ofmechanisms/methods/sources. In addition, control over this data does notnecessarily have to be by the (primary) user or account holder. Indeed,in addiction-related embodiments in particular, different data sets canbe controlled by someone other than the key user (e.g. addict, etc.).One embodiment of such in location-based security is the use of anaddict monitor or controller. Such an entity can be a human (or evenartificial entity) that is responsible for monitoring the addict over acertain time period. The idea is to maximize or at least increase theprobability that if a relapse were to happen, there would be a personon-call that would be at least generally aware of what the addict wasdoing, or at least have no uncertainty that if a high-risk/relapsesituation were to occur that they are #1 on the list of support personsto respond. This entity may be a human or an artificial intelligenceentity that has the responsibility of being at the top of the addictsupport hierarchy during a given time period if the addict were toencounter a high-risk situation during that time. The general purpose ofsuch controllers is multi-faceted: to distribute security of addictinformation across different entities as a general security precaution;make it progressively more difficult for hacker to access the data; and,in the case of addiction-related data provide security control toentities that are (almost literally) more sober than the actual user.

A side aspect of the controller function is that the controller'slocation/context would be sampled or otherwise tracked periodically forthe time-period when they are on-duty. This location/contextinformation, besides being used in identifying/locating support personif needed, could also be used to create a location/context-basedpassword for the addict's location/context—based on the controller'slocation/context—during the period of time the controller was on-duty.

While the location-based security and privacy elements and embodimentsdescribed in the present disclosure are primarily intended to protectinformation gathered in relation to getting/keeping an addict sober, itsuse is not limited to such addiction-related purposes. For example, oneset of embodiments of this location-based security and privacydisclosure is in using recent and/or historical location/contextinformation as a password reset verification mechanism, to prove that auser is not a robot, and/or to verify financial transaction. In one ofits simplest forms, such verification would consist of a system (orperson) asking where a person was on a certain date and/or certain time.Note that this query and response mechanism could be done using any ofthe user interface forms described elsewhere in the present disclosure.

Below is a variety of embodiments and examples that illustratelocation-based security and privacy concepts applicable to many types ofsituations, verification environments, and protections/access tofinancial, addiction-related, or other types of sensitive data. Most(but not all) such example data protection/access mechanisms have somesort of qualifiers to limit the scope of a location/context-relatedquestion or statement.

For example, Grid answers to the question could “Select the location &activities you were doing on Jul. 26, 20XX.” Note that the specificityof the question/statement can be tailored to the User'sLocation/Context-based Privacy and Security (P/S) Memory Profile, sothat questions/statements are not limited too narrowly (or broadly) forrecognition purposes:

-   -   As an easy example, Grid #4 could be an image of the user's        mother's house—a location and associated context (where they        live currently, as it looked before after a remodel last year)        easily identifiable to the user but less so to others and        unlikely to be the current image in a readily accessible        databases (e.g. Zillow, Google Maps, etc.). Such images could        also be taken from angles (such as the backyard) that are        generally not accessible on those kinds of databases,        particularly since such databases do not generally have linkages        to specifically identified family members and associated        belongings. Additional security could be added that requires the        user to identify how the image was captured (in this case from        User's Device A). It may be that a user may have many devices        (particularly for an Internet of Things user), but only takes        pictures from 1 or 2 devices. This fact might be known only to        the valid user.    -   Grid #5 shows a vacation picture of the user's son on vacation        in Location X doing Activity/Context of mountain climbing, taken        with a Helmet Cam—also easily identifiable to the user but much        less so to others. This verification question statement could be        further qualified by asking for example images that “were on        vacation”, “shows 1 of your children”, “shows mountain climbing”        or “ocean”, “an activity you did 20 years ago”, etc.—knowledge        likely known only to the user, as would the device used, which        could be further obscured by using a nickname for the Helmet        Cam, such as “3rd Eye” or “Gorgon”.    -   Grid #1, shows a picture of a “North Carolina” street sign. The        image may or may not have been “seen” by the user's device;        rather, it is representative of whether the user had (or had        not) been in North Carolina on (or around) the 7/26/XX time        frame. Such an image could be generated from the raw data of a        navigation app (such as the user's car #1—Device B onboard        navigation system), combined with a geo-fence around the state        of North Carolina that generated a database reading when the        user crossed the state boundary on 7/26/XX.    -   Grid #2 shows a textual representation of an address visited on        7/26/XX, such as the street address of the user's mother's house        in Grid #4, derived from that image's latitude/longitude through        a latitude/longitude to address converter program.    -   Grid #3 shows an algorithmic derivative and associated graphical        image that was created from the latitude/longitude centerpoint        of all activity on a certain date. So in this case, the actual        location/context of the user is not displayed, and used in an        indirect manner, but the user could easily associate that they        were a) indeed traveling overseas, b) it was in Australia,        and c) it was on business (a hacker might assume it was for        vacation, which in this case would be incorrect).    -   Grid #6 shows am image that has multiple images of past context,        in this case a picture of an dining room in a former home with a        now deceased pet. This could alternatively be a possible answer        to questions about playing with pets in old homes contexts.    -   Grid #7 is an algorithmic representation of a crossroads that        the user passed in a certain timeframe. Alternatively, it could        be a subtle representation of a location/context, such as taking        a trip with my cousin Sophie to Santa Monica California.    -   Grid #8 illustrates how an image is not necessarily        photo/video/visual/graphical in nature. In this case, it is an        audio clip of “California Dreaming” by the Mamas and Papas as a        way of indicating a location or geo-fence, based on location        information taken in this case from a mobile social networking        post from Friend “e”. This illustrates how images do not have to        be directly sourced by or even known to a user—the key is to be        recognizable to the user/person trying to access the        account/data.    -   Grid #9 is an image pulled at random from a sample of retail        visit locations, with the address matched with the store name        and image (source: Internet). This could be in response to a        question of where the user did NOT shop in the last week for        example, or something even more subtle in response to a question        of “select store(s) in a retail strip mall where you've shopped        the last week”, assuming the person knows that there is such a        store right next to a frequent shopping destination he or she        went to, even if they did not go to that specific store during        the timeframe in question.    -   Grid #10 illustrates an image taken from an indirect source (in        this case for example a child in the backseat of a car). The        driver may not have known such an image was being captured, but        enough detail about the driver (user) and his context is shown        that he would be able to recognize key location/context        questions (such as what car was this picture taken from, or what        state was the car in at the time of this picture?).    -   Grid #11 is another example of an externally sourced image—a        satellite image of a house from Google Satellite. Such images        could be asked in a verification process regarding valid family        member homes. It is likely only a user that is very familiar        with such homesteads could quickly identify such homes, making        more difficult for a bot to answer correctly.    -   Grid #12 shows how colloquial references to a location can be        used as a verification mechanism. In this case, like Grid #'s 2        and #4, it is intended to represent the user's mother's home.        This colloquial/textual representation could be used as a        stand-alone answer to a verification question or be paired with        other images such as asking the user to select all images        related to a family member. In addition, a family tracking        application such as AT&T Familymap can be used to extract        colloquial names and locations of key places, such as “Mom's        House”, “Kids School”, etc. These terms can then be        paired/matched to actual or derived images of those locations,        and some or all of the results presented as options in the        Verification Process.    -   Grid #13 shows how the quality of an image (or lack thereof) can        be used as a verification mechanism, under the premise that a        poorer quality image will be more difficult to analyze by a bot        or other hacking mechanism. Grid #13 is actually a much poorer        version of the image of the user's mother's house in Grid #4.        While still recognizable to the user, the image is likely to        just appear as a bunch of squiggly lines to a bot or        non-authorized user. Quality variations could be done in        numerous different ways, including changing image fill/lines        colors or solidarity (e.g. dashes instead of lines), color to        black and white, pixel density, and/or circus mirror-type        distortions.    -   Grid #14 shows a three-dimensional image of a roller coaster        ride, illustrating how 3D images (including dynamic images such        as video) can be used as both as a type of source data (e.g.        requiring 3D-capable data capturing mechanisms) but also images        where the person/entity trying to access the data has to have        the correct devices to appropriately view/select the image. In        some cases for example access to images could only be        practically possible through viewing through 3D glasses—in        effect prohibiting bots or automated/computerized mechanisms        from being able to process such images.    -   Grid #15 extends the 3D concepts of Grid #14 even further by        portraying possible verification images in a dynamic, 3D manner,        such as a rotating Rubik' s cube type presentation where images        are not only presented in 3D but also done in a temporary,        rotating manner that requires very fast decision making This        concept is elaborated on further in FIGS. 13 and 14, where        solving Rubik's cube type images is required to verify an        account and/or gain access to the account/data and where        different portions of the cube are sources/controlled by        different users/sources involved in the data collection and/or        usage process.    -   Grid #16 illustrates the potential role and sourcing (and        security and privacy concerns) associated with smart homes and        the Internet of Things (IoT), as common household items such as        TVs evolve beyond being dumb or one-way communications        mechanisms to being able to collect, store, and transmit        location/context and other data about a user (TV viewer). The        image in Grid #16 is a possible example of a picture/video that        could be taken of a TV viewer by the TV itself. As shown also in        FIG. 1, IoT may play a major role in collecting        location/contextual data (particularly activity/behavioral data)        in places/situations not historically available to such data        collection. This type of household data has the potential for        being particularly sensitive, and as such needs the extra        protections offered by the location-based privacy and security        mechanisms, systems, and methods described herein.

To successfully use the protections and security described by thislocation-based privacy and security, there needs to be ways ofverifying, authorizing, or otherwise providing access to the very large(and often very sensitive) volumes of data collected. At its simplest, alocation-based key (or password) needs to be matched to the correctimage/answer in order to proceed further. There are numerous ways ofmatching two images to see if they are the same. Many have to do withthe degree of uniqueness, e.g. do the two images share the same unusualrepresentation or pattern—sometimes done on a pixel-by-pixel basis.

For exemplary embodiments of the present disclosure, doing exact matchesare fairly straightforward, as the images being offered as averification/password matching option may often be the exact imagestored in a reference database, with few if any technical differences.Where degree of uniqueness comes into play is when certainlocation/contextual elements are the focus of the verification questionor password sequence, such as requesting all images that showchildren-at-play in my backyard during summer of 20XX. In those cases,the possible images not only may not be an exact duplicate of abaseline/reference image, there may not be a baseline/reference image,and/or the possible correct images may seem very different to thetypical viewer. In these cases, it can be subset(s) of the images thatwould be important—those portions of the image that determine keylocation/context elements, such as “children”, “at play” and “summer2000.” In those cases, a person may designate the important elements, ora matching algorithm may pick out key elements, such as the presence ofa swing set to be the proxy for “play”, “children” being anyone under 5feet tall (this needing a reference height), or “Summer 2000” showingtrees and grass in full bloom, with a date stamp of June, July, August,or September 2000 as part of the image metadata. A statisticalprobability technique may also apply, such that any match with more thana 90% probability would be considered sufficient.

A new way of dealing with the variances/uncertainties ofmatching/verifying images that are not technically/digitally the sameyet have sufficiently matching elements is location/contextfingerprinting, described in the present disclosure. As background, inthe wireless field, there is a concept called Radio Frequency (RF)Fingerprinting. This is a generally a location-determination methodwhere surrounding cellular or Wi-Fi signals at a given point aremeasured (such as measuring signal strength, or time-differences), andthose measurements stamped with a GPS location—constituting afingerprint of that location. When a user of such a system later reportsa variety of signal measurements, they can be compared to thefingerprint database, and if they match a fingerprint in the database,then that user is reported to be at the corresponding location.

A new variant of that concept is disclosed here. For example, averification database may have many hundreds or thousands of images of alocation and/or context—some drawn from/known to a user and others not.In a verification process, the verification engine could offer severalimages, and request the user to select the 3 that all have the samelocation or context in common. The common element could be any number ofthings—same location (home) or context (playing with my children), samelocation at the same point in time (summer last year), etc. It couldeven be which images were taken from the same (user) device—e.g.matching based on source device and/or metadata attached to the imageversus the image itself. All of these examples would be relatively easyfor the user to remember, and remember quickly; a hacker would have adifficult, even impossible job in deducing the correct answers. Like aphysical fingerprint is only on the user's physical person, theselocation/context fingerprints are available only in the user's brain. Asimple example of a location-based fingerprint is to have a front, side,and top image of the same object or situation that the user then has torecognize. For example, while such images of a house are readilyavailable from various sources (e.g. Google Earth, Zillow), suchdifferent perspectives of the same object would look very different tosomeone without a vested interest in the property (e.g. the owner), andthus it would likely only be an owner/resident that could quickly pickout 3 such images from a collection of several, for example.

The above is another instance of the importance and effectiveness oflocation/context-based privacy and security in protecting against robotsor bots seeking to compromise data/system security. For example, whenasked to verify whether or not a person is a robot, many such existingtechniques display several photos and ask the user to select those thatdisplay portions of street signs or store fronts. If the user correctlyselects all frames, then they are allowed to proceed with thetransaction. As an enhancement, the present disclosure could provideimages of locations personal to the user. For example, a variety ofstorefront images could be presented to the user, and the user asked toselect which ones they have been to within, say, the last 24 hours.These transactions could be selected according to a procedure,algorithm, or even randomly from the locations collected from the userduring that time period. This kind of verification would deter asophisticated robot as the validation process would be based on theuser's personal experiences and not a robot's general image recognitioncapability. Similarly several street signs with a real orsystemically-overlaid image of a street or road could be displayed, andask the user to select which streets/roads the user has traveled in thelast week. The system could provide as much granularity as needed inselecting the roads, whereas a “week” could be very specifically 7 days,or generally several days, depending on how good the user's memory is(and which is described in their memory profile discussed shortly).

Such a query could be structured to include locations/streets/roads thatthe system knows definitively has not been to during the proscribed timeperiod. This could create by a simple geo-fence-type algorithm thatencapsulates a user's movements in a particularly geographicaldesignation, such as town, city, zip code, county, or state for example,within a certain time period, and then providing other selection optionsclearly outside those areas during that time. A simple illustrationcould ask the person “have you been to O′Fallon, Illinois recently?” Thesystem's data store would know if the user has ever been there, but suchanswer is not part of public record such as asking where you've lived inthe past or even the model of car that you have owned. The verificationis based on the user's personally experienced location/contextinformation unlikely to be available in other databases that areaccessible by hackers.

The selection of the location transaction to base a query on could bedone in a variety of ways. The selection of the preferred method couldbe established beforehand in user profiles, for example, giving the userchoices to select locations for verification purposes based on day (e.g.Saturday), time (only in the afternoon), time period (within the lastday, week, or month), historical only (only last year's locations),geography (only Missouri locations), and/or context (e.g. locations whenI had been on vacation and/or clearly engaged in leisure activities).This allows the user to be prompted with locations that they are mostlikely to remember, yet with little or no obviousness to a hacker.

Indeed, the above could be used not just for verification or resetpurposes, but as an application/system password or key itself. Everytime a person logs onto Application B for example, instead of beingasked for an alphanumeric-based password, it could be shown as severalimages—individual or parts of images, e.g., as shown in the jigsaw orRubik's cube puzzles in FIGS. 11 and 13. Images are not limited solelyto visual/graphical items. The images may be visual, audio,alphanumeric, or other types of passwords depending on the userinterface, but are nonetheless referred to as images for convenience. Inwhatever form, the images are location and/or context-based and/orpersonally experienced by or known to the user. Several images could bedisplayed to the user, and the user may be asked to select the one(s)they've experienced at or during a selected time period. Or a timeperiod could be displayed with images all experienced or known to theuser, then the user may be asked to select the correct time-period froma list of options. An incorrect selection would be replaced by newoptions with a new answer that is based on the user's location/contextexperience or knowledge.

The user interface used in the verification/password selection processcan do more besides providing a large variety of image and image-typeselections—it can also enable new types of verification methods. Forexample, a 3D touch-type screen could enable choosing the correct imagesas they are raised or elevated in the view screen, and the user cantouch/press those images that are correct/in the correct sequence, in akind of virtual whack-a-mole mechanism. This concept is illustrated inFIG. 14. An example embodiment of this concept is that the user ispresented with a static or rotating image (or cube) that has one or morepictures being enhanced (e.g., popped-up, etc.) every few seconds. Theperson trying to gain access to the data/account/system would only have,for example, 2-3 seconds to select (virtually whack) the validoption(s)—validity being dependent on the verification/authorizationquestion or statement, such as “select all images showing your kidsplaying in your backyard in summer 20XX.” This is an example of aquestion that would be relatively easy for a valid user to answerbecause the user could readily recognize the user's own children, ownbackyard, and even season/year (if, for example, the user's backyard wasa landscaping mess up to Spring of that year, and a new swing setreplaced the old swing set the winter of 20XX). Conversely, this wouldbe very difficult if not impossible for a bot or other hacking-typealgorithm to discern the correct answer(s).

To provide further protection, the location data used could be selectedrandomly from the historical data store both in time and/or place. Alive, real-time, or near-live/real-time (e.g. only a few seconds,minutes, hours-old, etc.) stream or recording could also be used,requiring the user to relatively instantaneously or instantly recognizeimages not seen or recorded anywhere else because they are happening nowor just happened.

In exemplary embodiments associated with addiction, there are additionallocation-based security embodiments above and beyond (or particularlytailored) to addiction-related issues and/or data sensitivity. Forexample, one or more specific persons may be provided with control of alocation-key that is based on some personal information of the addictand/or support person(s). This key could be relatively static, changingrelatively infrequently, or dynamic, changing perhaps every day or evenhour. The premise is that much—even most—of an addict's data will gounused, or used very infrequently—thus it is not necessary to have acommonly-known, even easily accessible password or equivalent. Becauseexemplary embodiments of the present disclosure are generally iscentered around the prevention of relapses/usage of substance oractivity, if during a given period of time there has been nousage-related activity, there is little reason to retain thatinformation once key addict location/context/behavior information (suchas rewards-eligible behavior calculations) has been extracted. Once thedata has been fully used, it can then be erased in a Snapchat-likemanner, or archived with a location-based password or key, or pointer towho has the password or key. Or, portions of the data could be randomlyselected (or selected based on the user's memory profile) and stored forfuture verification/location-based security purposes. Depending on theaddict, addict-situation, controller or controller-situation, or otherfactors such as court-orders or law enforcement requirements, knowledgeof and/or access to location/context-related keys could be bypassed (orenhanced) to make it easier (or more difficult) for an addict'slocation/context data to be readily accessed. In certain circumstances,the addict's consent could be an absolute requirement for anyone toaccess the data—in other circumstances (e.g. court orders) the addict'sconsent might not be required at all.

The duration or longevity of location/context-related data, particularlyaddiction-related data, is also significant. As indicated in the processflow in FIG. 12, not all location/context data would be storedindefinitely. To the contrary, once the utility of an addict'slocation/context data has been fully utilized—either in detecting and/orpreventing and/or dealing with a high risk/relapse situation, orlearning/adjusting/modifying applicable learning mechanisms, as well asrewards, there may not be any reason to (continue to) store the data andthus can be deleted in Snapchat-type fashion. On the other hand, ifthere is a longer-term reason to store such data (court orders, a desireby the addict to journal addiction recovery, etc.), thenlocation/context-based privacy and security measures would be put inplace.

If location/context data for an addict is indeed archived, in general itshould not be easy to retrieve and be extension very difficult fornon-authorized persons or programs to access. Location-based security,with different pieces of historical data protected by differentlocation-based security mechanisms and passwords known to differentpersons, would achieve this high level of protection. To resurrect a 24hour period for example may require location-based passwords fromseveral different people—an electronic version of old-style banklock-boxes that require multiple keys to be inserted at the same time toopen the box. Instead, to retrieve the location data from Feb. 18, 20XXfor addict A, it may require location-based passwords from the threedifferent addict controllers that were on-duty that day, as well as theaddict themselves, to reassemble/reconstruct that day.

Images used in a visual matching scheme such as in FIG. 11 need not beones actually taken in photo form by a user or otherwise captured in agraphical form. For example, if the user had been detected at being atthe intersection of Santa Monica Blvd and Sophie Street within anacceptable time frame (according to the user's memory profile), a streetsign could be generated showing an street intersection sign image. Suchimages could even be generated using even more abstract mechanisms, suchas a mileage street sign showing how far to certain locations could bederived by a visited location, then displayed in visual form. Forexample, if a person was in Disney World in Orlando, Fla. 2 months ago,and their profile allows for vacation locations within the last year tobe used, a street sign could be shown that says 204 miles to Miami, 74miles to Tampa, etc.—those being the mileages between Orlando and thosecities. Thus, an image-based location key could be generated just usinga visited location or latitude/longitude.

The image selection process for the above embodiments can be simplisticor very difficult. On the simplistic end, for example, only one ofseveral images could be valid. On the difficult end, several could bevalid, but they must be selected in order of oldest to newest (or viceversa). The latter, for example, could be images (pictures, addresses,etc.) of previous home locations, pets (that were only alive at acertain property), etc. that only the user is likely to readily know inthe proper sequence, yet not require little additional effort by theuser to actually remember. Thus, it would be possible to create a verycomplex password or verification sequence, yet easy for the user tounderstand, and nearly impossible for an outside party to know, at leastwithout extensive research. A time or other context-based limit toprovide the answers could be included in the verification algorithm toprevent such research from successfully taking place.

Another exemplary embodiment or variation of the present disclosure uses3 dimensional, jigsaw-type verification. As seen in FIGS. 12, 13, and14, a location or context can be divided into numerous pieces, thenscrambled similar to a (2D or 3D) jigsaw puzzle, requiring the entirepuzzle to be solved, only certain pieces of it solved, or solved for aparticular type of solution, theme, and/or in a given time-sequence forexample. The general philosophy behind such puzzle approaches is that itbecomes progressively (even exponentially) more difficult for ahuman/entity not-familiar with the locations/contexts involved to solvethe puzzle, while being only incrementally more difficult for thosehumans/entities who are familiar with the locations/contexts involved.

That said, it is possible to use location-based concepts in thegeneration of a traditional password. For example, if the first 9 imagesin FIG. 11 were offered as possible answers to a verificationquestion/statement, and there were only two correct images out of the 9,with each frame being assigned a value from 1 to 9, then a point scorecould be calculated based on the values assigned to each image. A simpleexample would be if the two correct images had a value of 3 and 7,respectively, the resulting key/password could be thirty-seven(concatenation of the values) or twenty-one (the multiple). Thattraditional password adding potential capitalization and/or numbers,such as “Thirtyseven37”, could then be used by the various addiction (orother verification) analytical systems in accessing active data.

The various exemplary embodiments described above may provide anextremely sophisticated capability that establishes and systemicallyenforces privacy policies to support a balance of functionality andprivacy. There are at least two main types of privacy policy scenariosthat may be established and enforced. The first is where the addictvoluntarily signs up for functionality as disclosed herein. For such ascenario and associated policy, many of the more severe elements of suchfunctionality may be made optional, such as disabling paymentmechanisms, etc. A second scenario is an involuntary sign up of anaddict by other parties with the legal right, such as by parents ofminors, via judicial judgments, etc. In such cases, the functionality tobe activated may be decided by those parties with or without theaddict's consent or even without their knowledge (if deemed legal).Various exemplary embodiments may support and in some cases require thecoordination and integration of privacy and/or security policies andsystems by a host of parties: application(s) as disclosed herein;financial entities; support group entities (e.g. AA, etc.), publicsafety and law enforcement entities; education entities (e.g. for teens,etc.); retail/wholesale chains and individual stores, service areas(e.g. movie theaters, etc.) and services; individuals, and the like.

Various exemplary embodiments may provide functionality specific to agiven demographic. One example of this is for teenagers. Teenagers, andespecially teenage addicts—regardless of their addiction (though usuallydrugs or alcohol)—can have triggers and influencers in their lives thatare particularly distinct from other demographic categories: high schoolangst, peer pressure, parental pressure, academic pressure, sexualityissues, not to mention that most of the mechanisms for enabling theaddiction to begin with are illegal. Also, many addicts in that agedemographic have not reached the conclusion that they are an addict, letalone think they need help. They also are much more likely to betechnically savvy, which can be a double-edged sword. On the one hand,they would likely be loath to give up/not use those devices/mechanismsthat various embodiments may utilize (e.g. teenagers in this day and ageare almost tethered at the hip with their cell phone, with one of themost often used disciplinary methods used by parents is taking awaytheir cell phone privileges). On the other hand, teenagers are one of ifnot the most inventive demographic in getting around technical issuesand constraints on their activities. It is anticipated that teenagersmay represent a significant percentage of involuntary users of variousembodiments. Accordingly, in an example embodiment, social networks maybe used in detecting true friends (non-addicts or non-enablers) withthose who are technically friends in an addict's network but arerecognized by the addict, his family, and/or other entities (e.g.,judicial system, etc.) to be a negative influence on the addict. Forexample, permission may be given by these friends (particularly thenegative type) to transfer, copy, or otherwise apply that permission forthe addict to track the friend to the application of the exampleembodiment. Thus, the negative friend's location could be used in theexample embodiment to help the addict stay away from that so-calledfriend. This could be done with or without the addict's and/or thefriend's permission, e.g. supporting the voluntary and involuntaryprivacy scenarios described above.

Unlike smoking where addiction may occur relatively immediately,alcoholism generally takes much longer and is much more nuanced in itsprogression, hence alcoholics may have very long periods of denial. Inexemplary embodiments disclosed herein, sensors and other mechanisms maybe utilized for alcoholism testing for a person consciously or notand/or with or without the person's consent.

In various exemplary embodiments of the present disclosure, variousfunctionalities described herein may be integrated as a whole or inpart, e.g., into one or more methods, mechanisms, and/or applications.While any individual element above could be implemented individually, itis anticipated that much of the value of embodiments of the presentdisclosure is in the integration of the above, in whole or in part, toaccommodate the wide range of addiction enablers and alternatives, andthe various technology platforms that could be involved. Suchintegration may include other applications such as family finders,social networking applications, weather monitoring, navigationapplications, Facebook, Groupon, etc. Various embodiments can provideways in which to make the most of the moment of value, e.g. at timesand/or in context where an addict is in significant danger of relapsing.

That said, many features/aspects of the present disclosure are alsoanticipated to be of applicability to non-addicts or partiallyaddict-related scenarios, such as persons with common medicalconditions, sports enthusiasts, dating websites, law enforcement (e.g.additional functionality/flexibility beyond just GPS bracelets,providing other flexibility to the judicial system such providinginnovative options to judges in DUI cases to revoke their parole if theyare found to have stopped at a liquor store, etc.), medicalapplications, insurance applications, employee verification, medicalalerts, amber alerts, suicide prevention, etc. For example, bloodalcohol sensors may indicate a relapse by an alcoholic. Any one or moretriggers as disclosed herein may also or instead be used to identify anincreasing suicide risk and be monitored accordingly.

All of the above covers one or more addictions; a substantial portion ofthe addict community has more than one addiction. Also, it coversaddictions that may be replaced by others, such as replacing alcoholwith caffeine and/or sugar.

Exemplary embodiments are disclosed of systems and methods of usinglocation, context, and/or one or more communication networks formonitoring for, preempting, and/or mitigating pre-identified behavior.For example, exemplary embodiments disclosed herein may includeinvoluntarily, automatically, and/or wirelessly monitoring/mitigatingundesirable behavior (e.g., addiction related undesirable behavior,etc.) of a person (e.g., an addict, a parolee, a user of a system,etc.). In an exemplary embodiment, a system generally includes aplurality of devices and/or sensors configured to determine, through oneor more communications networks, a location of a person and/or a contextof the person at the location; predict and evaluate a risk of apre-identified behavior by the person in relation to the location and/orthe context; and facilitate one or more actions and/or activities tomitigate the risk of the pre-identified behavior, if any, and/or reactto the pre-identified behavior, if any, by the person.

The pre-identified behavior may include pre-identified addiction-relatedundesirable behavior, and the system may be configured to be operablefor monitoring for, preempting, and/or mitigating the pre-identifiedaddiction-related undesirable behavior. The system may be configured todetermine, through the one or more communications networks, a locationof an addict and/or a context of the addict at the location; predict andevaluate a risk of relapse by the addict in relation to the locationand/or the context; and facilitate one or more actions and/or activitiesto mitigate the risk of relapse, if any, and/or react to the relapse, ifany, by the addict. The system may be configured to determine whetherone or more addiction triggers predetermined in the system are active orpresent based on the location and/or the context and/or biometric,environmental, and/or behavioral data for the person. The system may beconfigured to determine whether one or more addiction triggerspredetermined in the system are active or present by comparing data fromone or more of the plurality of devices and/or sensors with one or moresettings for the person. The one or more settings for the person mayinclude one or more of blood pressure, heart rate, skin temperature,body temperature, respiratory rate, perspiration, weight, exerciseschedule, external temperature, noise loudness, and/or noise frequency.The plurality of devices and/or sensors may comprise one or morebiometric, environmental, and/or behavioral sensors that provide thebiometric, environmental, and/or behavioral data for the person usableby the system in determining whether one or more addiction triggerspredetermined in the system are active or present. The system may beconfigured to receive and process feedback and to adjust the pluralityof devices and/or sensors including increasing, decreasing, and/orotherwise modifying one or more of the settings and/or a frequency ofdata collection in response to the feedback including actions andbehaviors of the person associated with the data.

The system may be configured to predict and evaluate a risk of thepre-identified behavior by the person in relation to the location and/orthe context by using data from one or more of the plurality of devicesand/or sensors. The one or more of the plurality of devices and/orsensors may comprise one or more biometric, environmental, and/orbehavioral sensors. The one or more of the plurality of devices and/orsensors may comprise one or more of a blood pressure sensor, abreathalyzer, a blood alcohol content sensor, a thermometer, a skintemperature sensor, a breathing rate sensor, a heart rate sensor, a skinmoisture sensor, an olfactory sensor, a vestibular sensor, a kinestheticsensor, an optical sensor, a retinal scanner, a voice recognitionsensor, a fingerprint sensor, a facial recognition sensor, abiogestation sensor, an acoustic sensor, a microphone, a weather sensor,a barometer, a precipitation sensor, a gyroscope, an accelerometer,and/or a compass.

The one or more communications networks comprise an Internet of Thingsnetwork including one or more physical devices, items, vehicles, homeappliances, and/or household items usable by the system for determiningthe location of the person and/or the context of the person at thelocation. The system may be configured to determine the location of theperson via one or more of an Internet of Things network, a globalpositioning system, cell tower identification, cell tower triangulation,a beacon, radio frequency fingerprinting, real-time location services,Wi-Fi based location systems, radio frequency identification basedlocation systems, a drone, crowdsourcing, and/or simultaneouslocalization and mapping.

The system may be configured to predict and evaluate a risk of thepre-identified behavior by the person by using the location and/or thecontext and one or more of biometric, environmental, and/or behavioraldata of the person; voice data of the person and/or another person,including one or more of tone, inflection, cadence, tempo, and/orpre-identified words; and/or movement data, including the person'swalking gate, stride, and/or direction of travel; and/or date and/ortime of day; and/or historical visitation patterns of the person topredict and evaluate a risk of the pre-identified behavior by theperson; and/or monitoring social media.

The system may be configured to detect and track behavior of the personvia the plurality of devices and/or sensors to determine applicabilityand value of behavior and to provide a corresponding incentive ordisincentive for the person. The system may be configured to facilitateavoidance of one or more predetermined locations by omitting the one ormore predetermined locations from one or more navigation applicationsand/or by de-augmenting the one or more predetermined locations from oneor more augmented reality applications. The system may be configured toestablish one or more geo-fences for one or more predetermined locationsand to provide one or more alerts when the person crosses a geo-fence toenter or exit the predetermined location corresponding to the geo-fenceor otherwise violates the parameters associated with the geo-fence. Thesystem may be configured to use the plurality of devices and/or sensorsto assess a likelihood that the person is an alcoholic, drug addict,activity addict, and/or substance abuser.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network. The location of the personmay be a physical location or a virtual location. The context mayinclude a situation, an environment, and/or a state of mind of theperson based on one or more of biometric, environmental, and/orbehavioral data of the person.

The plurality of devices and/or sensors may include a plurality ofsensors configured to monitor the location and/or the context of theperson at the location. One or more of the plurality of sensors may belocated in, on, and/or near the person. A plurality of interface devicesmay be configured to engage in interaction with the person, with one ormore support persons for the person, and/or with one or more thirdparties in the event the system determines a relationship between thelocation and/or the context and one or more triggers predetermined inthe system that indicates a risk of the pre-identified behavior by theperson. The system may be configured to select the interaction based onthe one or more triggers and the location and/or the context of theperson at the location.

The system may be configured to develop and/or update a profile of theperson including one or more predetermined actions to implement for theperson depending on the prediction and evaluation of the risk of thepre-identified behavior by the person in relation to the location and/orthe context. The person may be a parolee, and the one or morecommunications networks allow the system to monitor the location of theperson both indoors and outdoors. The system may be configured to beusable by another one or more persons to voluntarily and/orinvoluntarily monitor the location of the person and/or the context ofthe person at the location.

The one or more actions and/or activities facilitated by the system mayinclude one or more of requesting the person to attend a nearbyaddiction support meeting, visit another one or more persons in asupport network, and/or travel to a predetermined location for a certainactivity; and/or providing an alert to a family member, medicalpersonnel, law enforcement, or other support person or persons; and/ordisabling a vehicle of the person; and/or automatically changingoperation of a vehicle of the person to driverless; and/or informing acommunity member or addiction sponsor of the person; and/or monitoringthe location of the person and a location of one or more support networkpersons and determining one or more scenarios that allow one or moresupport persons to be dispatched to the person's location or vice versa;and/or automatically playing a voice of a family member or a friend;and/or linking and coordinating an ad hoc meeting between the person andanother person, persons, or group; and/or providing a location-basedalternative and/or a location-based advertisement to the person via amobile phone; and/or provide linkages to a mobile phone that provide oneor more personal and/or impersonal reminders to the person aboutaddiction consequences.

The system may be configured to restrict and condition access to thesystem and/or to the person's data collected by one or more of theplurality of devices and/or sensors through the one or morecommunications networks based on selection of location-based data forthe person from a plurality of options presented by the system forselection, the plurality of options including the location-based dataand one or more other options. The system may be configured to use oneor more interface devices for interfacing with the person and/or todisseminate information to/from the person and/or one or more supportpersons for the person. The one or more interface devices may compriseone or more of tangible and/or tactile interfaces including one or moreof a display, illumination, sound, vibration, heat, and/or smellinterface.

The system may be configured to detect a relationship between thelocation and/or the context and one or more triggers predetermined forthe person as being related to the pre-identified behavior; and based onthe detected relationship, use one or more interface devices,mechanisms, or techniques to interact with the person, with one or moresupport persons for the person, and/or with a third party.

In an exemplary embodiment, a method for monitoring for, preempting,and/or mitigating pre-identified behavior generally includesdetermining, via one or more devices and/or sensors across one or morecommunications networks, a location of a person and/or a context of theperson at the location; predicting and evaluating a risk of apre-identified behavior by the person in relation to the location and/orthe context; and facilitating one or more actions and/or activities tomitigate the risk of the pre-identified behavior, if any, and/or reactto the pre-identified behavior, if any, by the person.

The method may include detecting a relationship between the locationand/or the context and one or more triggers predetermined for the personas being related to the pre-identified behavior; and based on thedetected relationship, using one or more interface devices, mechanisms,or techniques to interact with the person, with one or more supportpersons for the person, and/or with a third party.

The method may include determining whether the location and/or thecontext correspond to a high-risk location and context, then identifyingone or more potential actions and/or available support resources tomitigate the risk of the pre-identified behavior, selecting one or moreactions and one or more interfaces for the person, and implementing theselected action(s) and interface(s) for the person; and/or selecting andimplementing one or more actions and one or more interfaces for theperson if the location and/or the context indicate an immediate highrisk of the pre-identified behavior; and/or selecting and implementingone or more preventive actions for the person if the location and/or thecontext correspond to a trending risk of the pre-identified behavior orbehaviors, and/or adjusting and continuing to monitor the person'slocation and context at the location.

The method may include determining, projecting, or predicting a currentor future context of the person at the location by analyzing and linkingreal-time data and historical data for the person, the real-time andhistorical data including the location of the person, data from the oneor more devices and/or sensors, historical context of the person at thelocation, behavior patterns, travel patterns, health data, and riskcalculations; and/or monitoring the person's physical and mentalcondition via the one or more devices and/or sensors including one ormore wearable sensors and/or embedded sensors.

Facilitating one or more actions and/or activities may comprisedetermining which one or more devices and/or sensors are in use;determining available interfaces on the one or more devices and/orsensors that are determined to be in use; determining an inventory ofpotential interfaces desired by selected actions and that satisfy aprivacy requirement and/or live 2-way communication requirement; andselecting and implementing one or more interfaces from the inventory ofpotential interfaces.

The method may include determining, through the one or morecommunications networks, a location of an addict and/or a context of theaddict at the location; predicting and evaluating a risk of relapse bythe addict in relation to the location and/or the context; andfacilitating one or more actions and/or activities to mitigate the riskof relapse, if any, and/or react to the relapse, if any, by the addict.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network. The location of the personmay be a physical location or a virtual location. The determination ofthe context may be based on one or more of biometric, environmental,and/or behavioral data of the person. The pre-identified behavior mayinclude pre-identified addiction-related undesirable behavior. Themethod may include monitoring for, preempting, and/or mitigating thepre-identified addiction-related undesirable behavior. The method mayinclude determining whether one or more addiction triggers are active orpresent based on the location and/or the context and/or biometric,environmental, and/or behavioral data for the person.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprises computer-executable instructions for monitoring for,preempting, and/or mitigating pre-identified behavior, which whenexecuted by at least one processor, cause the at least one processor to:determine, via one or more devices and/or sensors across one or morecommunications networks, a location of a person and/or a context of theperson at the location; predict and evaluate a risk of a pre-identifiedbehavior by the person in relation to the location and/or the context;and facilitate one or more actions and/or activities to mitigate therisk of the pre-identified behavior, if any, and/or react to thepre-identified behavior, if any, by the person.

The one or more communications networks may include one or more of alocal network, a public network, a private network, the internet, and/orthe Internet of Things. The location of the person may be a physicallocation or a virtual location. The determination of the context may bebased on one or more of biometric, environmental, and/or behavioral dataof the person. The pre-identified behavior may include pre-identifiedaddiction-related undesirable behavior.

Also disclosed are exemplary embodiments of systems and methods forproviding location-based security and privacy for restricting useraccess. In an exemplary embodiment, a system is configured to restrictand condition access to the system and/or data based on a user'sselection of location-based data from a plurality of options presentedby the system for selection by the user. The plurality of optionsinclude the location-based data and one or more other options that areselectable by the user.

The system may be configured to present one or more queries and/orqualifiers to prompt the user to select corresponding location-baseddata from the plurality of options in response to the one or morequeries and/or qualifiers. The system may be configured to restrict theuser's access to the system and/or data at least until the correspondinglocation-based data is selected that satisfies the one or more queriesand/or qualifiers. The location-based data may comprise one or moreimages that satisfy the one or more queries and/or qualifiers. The oneor more other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured to restrict the user's access to the system and/or data atleast until the corresponding one or more images are selected thatsatisfy the one or more queries and/or qualifiers, or when the one ormore other images are selected that do not satisfy the one or morequeries and/or qualifiers. The system may be configured so as to notrestrict the user's access to the system and/or data when thecorresponding one or more images are selected that satisfy the one ormore queries and/or qualifiers.

The location-based data may include a location of the user and/or acontext of the user at the location as determined by the system usingone or more of a plurality of user devices and/or sensors across one ormore communications networks. The location-based data may comprise dataobtained by the system via an Internet of Things network of physicaldevices, items, vehicles, home appliances, and/or household items usableby the system for determining a location of the user and/or a context ofthe user at the location. The location-based data may comprise one ormore images based on a location and/or a context of the location toand/or known by the user, whereby the one or more images are usable bythe system as one or more passwords or keys for permitting access to theuser data.

The plurality of options may comprise a plurality of images presented bythe system for selection by the user. The location-based data compriseone or more images based on a location and/or a context of the locationto and/or known by the user. The images may comprise one or more of avisual, audio, graphical, video-based, photographic, textual, and/oralphanumeric image; a sensor reading; a static image; a dynamic image; amultidimensional image; a past image; a present image; a future image; alive streaming image; an image of a vacation destination; an image of afamily member; an image of a pet; an image of a vehicle; an image of aresidence; an image of a location; and/or a virtual or augmented realityimage; and/or; a drawing; a distorted image; a modified image; and/or anartificially rendered image.

The system may be configured to present a multidimensional (e.g., 2D,3D, 4D, etc.) combination puzzle that includes one or more keys and thatis successfully completed when corresponding location-based data isselected from the plurality of options for the one or more keys. Thesystem may be configured to restrict the user's access to the systemand/or data at least until the successful completion of themultidimensional combination puzzle. The system may be configured topresent one or more queries and/or qualifiers to prompt the user toselect, for the one or more keys, the corresponding location-based datafrom the plurality of options in response to the one or more queriesand/or qualifiers. The system may be configured such that themultidimensional combination puzzle is successfully completed when thecorresponding location-based data is selected for the one or more keysthat satisfy the one or more queries and/or qualifiers. Thelocation-based data may comprise one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers. The one ormore other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured such that the multidimensional combination puzzle issuccessfully completed when the corresponding one or more images areselected for the one or more keys that satisfy the one or more queriesand/or qualifiers. The system may be configured such that themultidimensional combination puzzle comprises a three-dimensional cubethat is successfully completed when the corresponding one or more imagesare selected for the one or more keys for at least one or more faces ofthe three-dimensional cube. The system may be configured to present theplurality of options to the user for selection as the one or more keysin a rotating, moving, and/or changing manner (e.g., zooming in/out,distorted, etc.) and/or for a predetermined amount of time. The systemmay be configured such that the multidimensional combination puzzlecomprises a two-dimensional grid or a jigsaw puzzle that is successfullycompleted when the corresponding location-based data is selected fromthe plurality of options for the one or more keys in a predeterminedorder or sequence.

The system may be configured to present one or more queries and/orqualifiers to prompt the user to select corresponding location-baseddata from the plurality of options in response to the one or morequeries and/or qualifiers. The location-based data may comprise one ormore images that are based on a location and/or a context of thelocation to and/or known by the user and that satisfy the one or morequeries and/or qualifiers. The one or more other options may compriseone or more other images that do not satisfy the one or more queriesand/or qualifiers. The system may be configured to restrict the user'saccess to the system and/or data at least until the selection of thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers and the corresponding one or more devices used tocapture the corresponding one or more images.

The location-based data may comprise a plurality of different images ofa location and/or context of the location to and/or known by the user.The one or more other options may comprise one or more other images. Thesystem may be configured to present one or more queries and/orqualifiers to prompt the user to select the corresponding images of thelocation and/or context in response to the one or more queries and/orqualifiers. The system may be configured to restrict the user's accessto the system and/or data at least until the corresponding images of thelocation and/or context are selected that satisfy the one or morequeries and/or qualifiers. The system may be configured to present oneor more queries and/or qualifiers to prompt the user to selectcorresponding location-based data from the plurality of options inresponse to the one or more queries and/or qualifiers. Thelocation-based data may comprise one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers. The one ormore other options may comprise one or more other images that do notsatisfy the one or more queries and/or qualifiers. The system may beconfigured to assign a numerical value to the one or more images and tothe one or more other images. The system may be configured to use thenumerical value(s) of the corresponding one or more images selected bythe user that satisfy the one or more queries and/or qualifiers forgenerating a key or password for accessing the user data.

The location-based data may include recent and/or historical locationand/or context information of the user. The system may be configured touse the recent and/or historical location and/or context information ofthe user for a password reset verification, to prove that a user is nota robot, and/or to verify a financial transaction.

The system may be configured to restrict and condition access tobiometric data, environmental data, behavioral data, and/orlocation-based data for a person, obtained by the system via one or moreof a plurality of devices and/or sensors through one or morecommunications networks, based on the user's selection of location-baseddata for the person from the plurality of options presented by thesystem for selection by the user. The user may be the person, anotherperson, and/or an accessor.

The system may include a plurality of devices and/or sensors configuredto determine, through one or more communications networks, a location ofa person and/or a context of the person at the location; predict andevaluate a risk of a pre-identified behavior by the person in relationto the location and/or the context; and facilitate one or more actionsand/or activities to mitigate the risk of the pre-identified behavior,if any, and/or react to the pre-identified behavior, if any, by theperson. The system may be configured to restrict and condition access todata for the person, obtained via one or more of the plurality ofdevices and/or sensors through the one or more communications networks,based on a user's selection of location-based data for the person fromthe plurality of options presented by the system for selection by theuser, whereby the user is the person, another person, and/or anaccessor.

The system may be configured to restrict and condition access to aperson's data based on the user's selection of location-based data forthe person from the plurality of options presented by the system forselection by the user. The user may be the person, another person,and/or an accessor. The system may comprise a non-transitorycomputer-readable storage media including computer-executableinstructions, which when executed by at least one processor, cause theat least one processor to present the plurality of options for selectionby the user including the location-based data and the one or more otheroptions; determine whether the user selected the location-based datafrom the plurality of options; and restrict access to the system and/ordata at least until it is has been determined that the user selected thelocation-based data from the plurality of options.

In another exemplary embodiment, a method for providing security and/orprivacy generally includes presenting a plurality of options forselection by a user, the plurality of options including location-baseddata and one or more other options; determining whether the userselected the location-based data from the plurality of options; andrestricting the user's access to a system and/or data at least until itis has been determined that the user selected the location-based datafrom the plurality of options.

The method may include presenting one or more queries and/or qualifiersto prompt the user to select corresponding location-based data from theplurality of options in response to the one or more queries and/orqualifiers; determining whether the user selected the correspondinglocation-based data that satisfies the one or more queries and/orqualifiers; and restricting the user's access to the system and/or dataat least until it has been determined that the user selected thecorresponding location-based data that satisfies the one or more queriesand/or qualifiers.

The method may include presenting one or more images that are based on alocation and/or a context of the location to and/or known by the userand that satisfy the one or more queries and/or qualifiers; presentingone or more other images that do not satisfy the one or more queriesand/or qualifiers; and determining whether the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers; restricting the user's access to the system and/ordata at least until it has been determined that the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers.

In an exemplary embodiment, a non-transitory computer-readable storagemedia comprises computer-executable instructions for providing securityand/or privacy, which when executed by at least one processor, cause theat least one processor to restrict and condition access to a systemand/or data based on a user's selection of location-based data from aplurality of options presented for selection by the user, the pluralityof options including the location-based data and one or more otheroptions that are selectable by the user.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present the pluralityof options for selection by the user including the location-based dataand the one or more other options; determine whether the user selectedthe location-based data from the plurality of options; and restrict theuser's access to the system and/or data at least until it is has beendetermined that the user selected the location-based data from theplurality of options.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present one or morequeries and/or qualifiers to prompt the user to select correspondinglocation-based data from the plurality of options in response to the oneor more queries and/or qualifiers; determine whether the user selectedthe corresponding location-based data that satisfies the one or morequeries and/or qualifiers; and restrict the user's access to the systemand/or data at least until it has been determined that the user selectedthe corresponding location-based data that satisfies the one or morequeries and/or qualifiers.

The computer-executable instructions, when executed by the at least oneprocessor, may cause the at least one processor to present one or moreimages that are based on a location and/or a context of the location toand/or known by the user and that satisfy the one or more queries and/orqualifiers; present one or more other images that do not satisfy the oneor more queries and/or qualifiers; determine whether the correspondingone or more images are selected that satisfy the one or more queriesand/or qualifiers; and restrict the user's access to the system and/ordata at least until it has been determined that the user selected thecorresponding one or more images that satisfy the one or more queriesand/or qualifiers.

Exemplary embodiments of the present disclosure can be implemented innumerous ways, including (without limitation) as method(s)/process(es),apparatus(es), system(s), composition(s) of matter, computer readablemedia, such as non-transitory computer readable storage media, and/orcomputer network(s) wherein program instructions may be sent, e.g., overoptical, electronic, wireline, cloud-based, drone-based, Internet,wireless, peer-to-peer, machine-to-machine, and/or other communicationslink(s) and combination(s). At least some such implementations may bereferred to, e.g., as techniques and/or mechanisms. In general, theorder of the steps of disclosed processes may be altered within thescope of the present disclosure.

Exemplary embodiments may include one or more computing devices, such asone or more servers, workstations, personal computers, laptops, tablets,smartphones, person digital assistants (PDAs), etc. In addition, thecomputing device may include a single computing device, or it mayinclude multiple computing devices located in close proximity ordistributed over a geographic region, so long as the computing devicesare specifically configured to function as described herein. Further,different components and/or arrangements of components than illustratedherein may be used in the computing device and/or in other computingdevice embodiments.

Exemplary embodiments may include one or more processors and memorycoupled to (and in communication with) the one or more processors. Aprocessor may include one or more processing units (e.g., in amulti-core configuration, etc.) such as, and without limitation, acentral processing unit (CPU), a microcontroller, a reduced instructionset computer (RISC) processor, an application specific integratedcircuit (ASIC), a programmable logic device (PLD), a gate array, and/orany other circuit or processor capable of the functions describedherein.

In exemplary embodiments, a memory may be one or more devices thatpermit data, instructions, etc., to be stored therein and retrievedtherefrom. The memory may include one or more computer-readable storagemedia, such as, without limitation, dynamic random access memory (DRAM),static random access memory (SRAM), read only memory (ROM), erasableprogrammable read only memory (EPROM), solid state devices, flashdrives, CD-ROMs, thumb drives, and/or any other type of volatile ornonvolatile physical or tangible computer-readable media.

In exemplary embodiments, computer-executable instructions may be storedin the memory for execution by a processor to particularly cause theprocessor to perform one or more of the functions described herein, suchthat the memory is a physical, tangible, and non-transitory computerreadable storage media. Such instructions often improve the efficienciesand/or performance of the processor that is performing one or more ofthe various operations herein. It should be appreciated that the memorymay include a variety of different memories, each implemented in one ormore of the functions or processes described herein.

In exemplary embodiments, a network interface may be coupled to (and incommunication with) the processor and the memory. The network interfacemay include, without limitation, a wired network adapter, a wirelessnetwork adapter, a mobile network adapter, or other device capable ofcommunicating to one or more different networks. In some exemplaryembodiments, one or more network interfaces may be incorporated into orwith the processor.

It should be appreciated that the functions described herein, in someembodiments, may be described in computer executable instructions storedon a computer readable media, and executable by one or more processors.The computer readable media is a non-transitory computer readablestorage medium. By way of example, and not limitation, suchcomputer-readable media can include RAM, ROM, EEPROM, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium that can be used to carry or store desiredprogram code in the form of instructions or databases and that can beaccessed by a computer. Combinations of the above should also beincluded within the scope of computer-readable media.

It should also be appreciated that one or more aspects of the presentdisclosure transform a general-purpose computing device into aspecial-purpose computing device when configured to perform thefunctions, methods, and/or processes described herein.

Example embodiments are provided so that the present disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms, and that neither should be construed to limit the scope of thepresent disclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail. For example, technical material that is known inthe technical fields related to the present disclosure has not beendescribed in detail so that the present disclosure is not unnecessarilyobscured. This includes, but is not limited, to technology utilized indetermining the location of mobile devices via a variety of means. Inaddition, advantages and improvements that may be achieved with one ormore exemplary embodiments of the present disclosure are provided forpurposes of illustration only and do not limit the scope of the presentdisclosure, as exemplary embodiments disclosed herein may provide all ornone of the above mentioned advantages and improvements and still fallwithin the scope of the present disclosure.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. In addition, as used herein, the term “or” is an inclusive“or” operator, and is equivalent to the term “and/or,” unless thecontext clearly dictates otherwise. The terms “comprises,” “comprising,”“including,” and “having,” are inclusive and therefore specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. The method steps, processes, andoperations described herein are not to be construed as necessarilyrequiring their performance in the particular order discussed orillustrated, unless specifically identified as an order of performance.It is also to be understood that additional or alternative steps may beemployed.

The term “based on” is not exclusive and allows for being based onadditional factors not described, unless the context clearly dictatesotherwise. The term “network” is used in multiple contexts within thepresent disclosure, and its use generally (but not necessarily) fallsinto one of two categories. The first is in the form of a (generallyhuman) support “network” including one or more individuals/entities thatprovide the addict with some sort of support or assistance. The secondis in a technical context, such as a communications network thattransmits, receives, and/or otherwise provides technical connectivitybetween various technical components disclosed herein.

As used herein, the terms “support network” and “support community”refer to a concept that an individual's or groups of individuals'personal network of friends, family colleagues, coworkers,medical/mental health/addiction professionals, members of their socialnetwork (e.g. Facebook, Twitter, Snapchat, etc.), etc. and thesubsequent connections within those networks can be utilized to findmore relevant connections for a variety of activities, including, butnot limited to dating, job networking, service referrals, contentsharing, like-minded individuals, activity partners, or the like. Suchsocial network may be created based on a variety of criteria, including,for example, an address book, a social event, an online community, orthe like. As used herein, the term “member” refers to a user who isincluded in a support network. The term “group” or “community” refers toa collection of members.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, or features, these elements,components, or features should not be limited by these terms. Theseterms may be only used to distinguish one element, component, or featurefrom another element, component, or feature. Terms such as “first,”“second,” and other numerical terms when used herein do not imply asequence or order unless clearly indicated by the context. Thus, a firstelement, component, or feature could be termed a second element,component, or feature without departing from the teachings of theexample embodiments.

None of the elements recited in the claims are intended to be ameans-plus-function element within the meaning of 35 U.S.C. § 112(f)unless an element is expressly recited using the phrase “means for,” orin the case of a method claim using the phrases “operation for” or “stepfor.”

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure. Individual elements,intended or stated uses, or features of a particular embodiment aregenerally not limited to that particular embodiment, but, whereapplicable, are interchangeable and can be used in a selectedembodiment, even if not specifically shown or described. The same mayalso be varied in many ways. Such variations are not to be regarded as adeparture from the present disclosure, and all such modifications areintended to be included within the scope of the present disclosure.

What is claimed is:
 1. A system for monitoring for, preempting, and/ormitigating pre-identified behavior, the system comprising a plurality ofdevices and/or sensors configured to: determine, through one or morecommunications networks, a location of a person and/or a context of theperson at the location; predict and evaluate a risk of a pre-identifiedbehavior by the person in relation to the location and/or the context;and facilitate one or more actions and/or activities to mitigate therisk of the pre-identified behavior, if any, and/or react to thepre-identified behavior, if any, by the person.
 2. The system of claim1, wherein: the pre-identified behavior includes pre-identifiedaddiction-related undesirable behavior; and the system is configured tobe operable for monitoring for, preempting, and/or mitigating thepre-identified addiction-related undesirable behavior.
 3. The system ofclaim 2, wherein the system is configured to: determine, through the oneor more communications networks, a location of an addict and/or acontext of the addict at the location; predict and evaluate a risk ofrelapse by the addict in relation to the location and/or the context;and facilitate one or more actions and/or activities to mitigate therisk of relapse, if any, and/or react to the relapse, if any, by theaddict.
 4. The system of claim 2, wherein the system is configured todetermine whether one or more addiction triggers predetermined in thesystem are active or present based on the location and/or the contextand/or biometric, environmental, and/or behavioral data for the person.5. The system of claim 4, wherein the system is configured to determinewhether one or more addiction triggers predetermined in the system areactive or present by comparing data from one or more of the plurality ofdevices and/or sensors with one or more settings for the person, andwherein: the one or more settings for the person include one or more ofblood pressure, heart rate, skin temperature, body temperature,respiratory rate, perspiration, weight, exercise schedule, externaltemperature, noise loudness, and/or noise frequency; and/or theplurality of devices and/or sensors comprise one or more biometric,environmental, and/or behavioral sensors that provide the biometric,environmental, and/or behavioral data for the person usable by thesystem in determining whether one or more addiction triggerspredetermined in the system are active or present.
 6. The system ofclaim 5, wherein the system is configured to receive and processfeedback and to adjust the plurality of devices and/or sensors includingincreasing, decreasing, and/or otherwise modifying one or more of thesettings and/or a frequency of data collection in response to thefeedback including actions and behaviors of the person associated withthe data.
 7. The system of claim 1, wherein the system is configured topredict and evaluate a risk of the pre-identified behavior by the personin relation to the location and/or the context by using data from one ormore of the plurality of devices and/or sensors, and wherein: the one ormore of the plurality of devices and/or sensors comprise one or morebiometric, environmental, and/or behavioral sensors; and/or the one ormore of the plurality of devices and/or sensors comprise one or more ablood pressure sensor, a breathalyzer, a blood alcohol content sensor, athermometer, a skin temperature sensor, a breathing rate sensor, a heartrate sensor, a skin moisture sensor, an olfactory sensor, a vestibularsensor, a kinesthetic sensor, an optical sensor, a retinal scanner, avoice recognition sensor, a fingerprint sensor, a facial recognitionsensor, a biogestation sensor, an acoustic sensor, a microphone, aweather sensor, a barometer, a precipitation sensor, a gyroscope, anaccelerometer, and/or a compass.
 8. The system of claim 1, wherein theone or more communications networks comprise an Internet of Thingsnetwork including one or more physical devices, items, vehicles, homeappliances, and/or household items usable by the system for determiningthe location of the person and/or the context of the person at thelocation.
 9. The system of claim 1, wherein the system is configured todetermine the location of the person via one or more of an Internet ofThings network, a global positioning system, cell tower identification,cell tower triangulation, a beacon, radio frequency fingerprinting,real-time location services, Wi-Fi based location systems, radiofrequency identification based location systems, a drone, crowdsourcing,and/or simultaneous localization and mapping.
 10. The system of claim 1,wherein the system is configured to predict and evaluate a risk of thepre-identified behavior by the person by using the location and/or thecontext and one or more of: biometric, environmental, and/or behavioraldata of the person; voice data of the person and/or another person,including one or more of tone, inflection, cadence, tempo, and/orpre-identified words; and/or movement data, including the person'swalking gate, stride, and/or direction of travel; and/or date and/ortime of day; and/or historical visitation patterns of the person topredict and evaluate a risk of the pre-identified behavior by theperson; and/or monitoring social media.
 11. The system of claim 1,wherein the system is configured to detect and track behavior of theperson via the plurality of devices and/or sensors to determineapplicability and value of behavior and to provide a correspondingincentive or disincentive for the person.
 12. The system of claim 1,wherein the system is configured to facilitate avoidance of one or morepredetermined locations by omitting the one or more predeterminedlocations from one or more navigation applications and/or byde-augmenting the one or more predetermined locations from one or moreaugmented reality applications.
 13. The system of claim 1, wherein thesystem is configured to establish one or more geo-fences for one or morepredetermined locations and to provide one or more alerts when theperson crosses a geo-fence to enter or exit the predetermined locationcorresponding to the geo-fence or otherwise violates the parametersassociated with the geo-fence.
 14. The system of claim 1, wherein thesystem is configured to use the plurality of devices and/or sensors toassess a likelihood that the person is an alcoholic, drug addict,activity addict, and/or substance abuser.
 15. The system of claim 1,wherein: the one or more communications networks include one or more ofa local network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network; and/or the location of theperson is a physical location or a virtual location; and/or the contextincludes a situation, an environment, and/or a state of mind of theperson based on one or more of biometric, environmental, and/orbehavioral data of the person.
 16. The system of claim 1, wherein theplurality of devices and/or sensors includes: a plurality of sensorsconfigured to monitor the location and/or the context of the person atthe location, one or more of the plurality of sensors being located in,on, and/or near the person; and a plurality of interface devicesconfigured to engage in interaction with the person, with one or moresupport persons for the person, and/or with one or more third parties inthe event the system determines a relationship between the locationand/or the context and one or more triggers predetermined in the systemthat indicates a risk of the pre-identified behavior by the person;whereby the system is configured to select the interaction based on theone or more triggers and the location and/or the context of the personat the location.
 17. The system of claim 1, wherein the system isconfigured to develop and/or update a profile of the person includingone or more predetermined actions to implement for the person dependingon the prediction and evaluation of the risk of the pre-identifiedbehavior by the person in relation to the location and/or the context.18. The system of claim 1, wherein: the person is a parolee, and the oneor more communications networks allow the system to monitor the locationof the person both indoors and outdoors; and/or the system is configuredto be usable by another one or more persons to voluntarily and/orinvoluntarily monitor the location of the person and/or the context ofthe person at the location.
 19. The system of claim 1, wherein the oneor more actions and/or activities facilitated by the system include oneor more of: requesting the person to attend a nearby addiction supportmeeting, visit another one or more persons in a support network, and/ortravel to a predetermined location for a certain activity; and/orproviding an alert to a family member, medical personnel, lawenforcement, or other support person or persons; and/or disabling avehicle of the person; and/or automatically changing operation of avehicle of the person to driverless; and/or informing a community memberor addiction sponsor of the person; and/or monitoring the location ofthe person and a location of one or more support network persons anddetermining one or more scenarios that allow one or more support personsto be dispatched to the person's location or vice versa; and/orautomatically playing a voice of a family member or a friend; and/orlinking and coordinating an ad hoc meeting between the person andanother person, persons, or group; and/or providing a location-basedalternative and/or a location-based advertisement to the person via amobile phone; and/or provide linkages to a mobile phone that provide oneor more personal and/or impersonal reminders to the person aboutaddiction consequences; and/or
 20. The system of claim 1, wherein thesystem is configured to restrict and condition access to the systemand/or to the person's data collected by one or more of the plurality ofdevices and/or sensors through the one or more communications networksbased on selection of location-based data for the person from aplurality of options presented by the system for selection, theplurality of options including the location-based data and one or moreother options.
 21. The system of claim 1, wherein: the system isconfigured to use one or more interface devices for interfacing with theperson and/or to disseminate information to/from the person and/or oneor more support persons for the person; and the one or more interfacedevices comprise one or more of tangible and/or tactile interfacesincluding one or more of a display, illumination, sound, vibration,heat, and/or smell interface.
 22. The system of claim 1, wherein thesystem is configured to: detect a relationship between the locationand/or the context and one or more triggers predetermined for the personas being related to the pre-identified behavior; and based on thedetected relationship, use one or more interface devices, mechanisms, ortechniques to interact with the person, with one or more support personsfor the person, and/or with a third party.
 23. A method for monitoringfor, preempting, and/or mitigating pre-identified behavior, the methodcomprising: determining, via one or more devices and/or sensors acrossone or more communications networks, a location of a person and/or acontext of the person at the location; predicting and evaluating a riskof a pre-identified behavior by the person in relation to the locationand/or the context; and facilitating one or more actions and/oractivities to mitigate the risk of the pre-identified behavior, if any,and/or react to the pre-identified behavior, if any, by the person. 24.The method of claim 23, wherein the method includes: detecting arelationship between the location and/or the context and one or moretriggers predetermined for the person as being related to thepre-identified behavior; and based on the detected relationship, usingone or more interface devices, mechanisms, or techniques to interactwith the person, with one or more support persons for the person, and/orwith a third party.
 25. The method of claim 23, wherein the methodincludes: determining whether the location and/or the context correspondto a high-risk location and context, then identifying one or morepotential actions and/or available support resources to mitigate therisk of the pre-identified behavior, selecting one or more actions andone or more interfaces for the person, and implementing the selectedaction(s) and interface(s) for the person; and/or selecting andimplementing one or more actions and one or more interfaces for theperson if the location and/or the context indicate an immediate highrisk of the pre-identified behavior; and/or selecting and implementingone or more preventive actions for the person if the location and/or thecontext correspond to a trending risk of the pre-identified behavior orbehaviors, and/or adjusting and continuing to monitor the person'slocation and context at the location.
 26. The method of claim 23,wherein the method includes: determining, projecting, or predicting acurrent or future context of the person at the location by analyzing andlinking real-time data and historical data for the person, the real-timeand historical data including the location of the person, data from theone or more devices and/or sensors, historical context of the person atthe location, behavior patterns, travel patterns, health data, and riskcalculations; and/or monitoring the person's physical and mentalcondition via the one or more devices and/or sensors including one ormore wearable sensors and/or embedded sensors.
 27. The method of claim23, wherein facilitating one or more actions and/or activitiescomprises: determining which one or more devices and/or sensors are inuse; determining available interfaces on the one or more devices and/orsensors that are determined to be in use; determining an inventory ofpotential interfaces desired by selected actions and that satisfy aprivacy requirement and/or live 2-way communication requirement; andselecting and implementing one or more interfaces from the inventory ofpotential interfaces.
 28. The method of claim 23, wherein the methodincludes: determining, through the one or more communications networks,a location of an addict and/or a context of the addict at the location;predicting and evaluating a risk of relapse by the addict in relation tothe location and/or the context; and facilitating one or more actionsand/or activities to mitigate the risk of relapse, if any, and/or reactto the relapse, if any, by the addict.
 29. The method of claim 23,wherein: the one or more communications networks include one or more ofa local network, a public network, a private network, the internet, theInternet of Things, a wireless network, a terrestrial network, asatellite network, and/or wireline network; and/or the location of theperson is a physical location or a virtual location; and/or thedetermination of the context is based on one or more of biometric,environmental, and/or behavioral data of the person; and/or thepre-identified behavior includes pre-identified addiction-relatedundesirable behavior; and/or the method includes monitoring for,preempting, and/or mitigating the pre-identified addiction-relatedundesirable behavior; and/or the method includes determining whether oneor more addiction triggers are active or present based on the locationand/or the context and/or biometric, environmental, and/or behavioraldata for the person.
 30. A non-transitory computer-readable storagemedia comprising computer-executable instructions for monitoring for,preempting, and/or mitigating pre-identified behavior, which whenexecuted by at least one processor, cause the at least one processor to:determine, via one or more devices and/or sensors across one or morecommunications networks, a location of a person and/or a context of theperson at the location; predict and evaluate a risk of a pre-identifiedbehavior by the person in relation to the location and/or the context;and facilitate one or more actions and/or activities to mitigate therisk of the pre-identified behavior, if any, and/or react to thepre-identified behavior, if any, by the person.
 31. The non-transitorycomputer-readable storage media of claim 30, wherein: the one or morecommunications networks include one or more of a local network, a publicnetwork, a private network, the internet, and/or the Internet of Things;and/or the location of the person is a physical location or a virtuallocation; and/or the determination of the context is based on one ormore of biometric, environmental, and/or behavioral data of the person;and/or the pre-identified behavior includes pre-identifiedaddiction-related undesirable behavior.